A New Approach for Classifying Fetal Growth Restriction

Effects of ambient air pollution on the risk of small- and large-for-gestational-age births: an analysis using national birth data in Japan

OBJECTIVES

Small-for-gestational-age (SGA) and large-for-gestational-age (LGA) births are major adverse birth outcomes related to newborn health. In contrast, the association between ambient air pollution levels and SGA or LGA births has not been investigated in Japan; hence, the purpose of our study is to investigate this association.

METHODS

We used birth data from Vital Statistics in Japan from 2017 to 2021 and municipality-level data on air pollutants, including nitrogen dioxide (NO2), sulfur dioxide (SO2), photochemical oxidants, and particulate matter 2.5 (PM2.5). Ambient air pollution levels throughout the first, second, and third trimesters, as well as the whole pregnancy, were calculated for each birth. The association between SGA/LGA and ambient levels of the air pollutants was investigated using crude and adjusted log-binomial regression models. In addition, a regression model with spline functions was also used to detect the non-linear association.

RESULTS

We analyzed data from 2,434,217 births. Adjusted regression analyses revealed statistically significant and positive associations between SGA birth and SO2 level, regardless of the exposure period. Specifically, the risk ratio for average SO2 values throughout the whole pregnancy was 1.014 (95% confidence interval [CI] 1.009, 1.019) per 1 ppb increase. In addition, regression analysis with spline functions indicated that an increase in risk ratio for SGA birth depending on SO2 level was linear. Furthermore, statistically significant and negative associations were observed between LGA birth and SO2 except for the third trimester.

CONCLUSIONS

It was suggested that ambient level of SO2 during the pregnancy term is a risk factor for SGA birth in Japan.

The placental blood perfusion and LINC00473-mediated promotion of trophoblast apoptosis in fetal growth restriction

This study aimed to investigate placental microblood flow perfusion in fetal growth restriction (FGR) both pre- and post-delivery, and explore the influence of LINC00473 and its downstream targets on FGR progression in trophoblast cells. Placental vascular distribution, placental vascular index (VIMV), CD34 expression, and histological changes were compared between control and FGR groups. FGR-related differentially expressed genes (DEGs) were analyzed and validated by quantitative real-time polymerase chain reaction (qPCR) and immunohistochemistry (IHC) in placentae. In vitro experiments examined the regulatory relationships among LINC00473, miR-5189-5p, and StAR, followed by investigations into their impacts on cell proliferation and apoptosis. FGR placentae exhibited irregular shapes, uneven parenchymal echo, stromal dysplasia, ischemic infarction, and variable degrees of thickening in some cases. FGR samples showed less prominent mother vessel lakes, significantly lower VIMV, and decreased CD34 expression. Hematoxylin & eosin (H&E) staining revealed placental fibrosis, fibrin adhesion, infarction, and interstitial dysplasia in FGR. LINC00473, miR-5189-5p, and StAR were identified as DEG, with qPCR demonstrating a significant increase in LINC00473 and a decrease in miR-5189-5p in FGR, while both qPCR and IHC indicated a significant increase in StAR expression. LINC00473 served as an endogenous sponge against miR-5189-5p in human HTR-8/SV neo cells, and StAR expression was regulated by both LINC00473 and miR-5189-5p. Dysregulation of these genes affected cell proliferation and apoptosis. Pathological changes in the placenta are significant contributors to FGR, with placental microblood flow potentially serving as an indicator for monitoring its progression. LINC00473 and its downstream targets may modulate trophoblasts proliferation and apoptosis, thus influencing the onset of FGR, suggesting novel avenues for diagnosis and treatment.

Expected and Desirable Preterm and Small Infant Growth Patterns

Adequate nutrition is necessary for achieving optimal growth and neurodevelopment. Growth is a natural and expected process that happens concomitantly with rapid advancements in neurodevelopment. Serial weight, length, and head circumference growth measures are essential for monitoring development, although identifying pathological deviations from normal growth can pose challenges. Appropriate growth assessments require considerations that a range of sizes for length, head circumference, and weight are expected and appropriate. Because of genetic differences and morbidities, there is a considerable overlap between the growth of healthy infants and those with growth alterations. Parents tend to be over-concerned about children who plot low on growth charts and often need reassurance. Thus, the use of terms such as "poor" growth or growth "failure" are discouraged when growth is approximately parallel to growth chart curves even if their size is smaller than specific percentiles. No specific percentile should be set as a growth goal; individual variability should be expected. An infant's size at birth is important information that goes beyond the common use of prognostic predictions of appropriate compared with small or large for gestational age. The lower the birthweight, the lower the nutrient stores and the more important the need for nutrition support. Compared to term infants, preterm infants at term-equivalent age have a higher percentage of body fat, but this diminishes over the next months. Current research findings support expert recommendations that preterm infants should grow, after early postnatal weight loss, similar to the fetus and then term-born infants, which translates to growth approximately parallel to growth chart curves. There is no need for a trade-off between optimum cognition and optimum future health. Each high-risk infant needs individualized nutrition and growth assessments. This review aims to examine infant growth expectations and messaging for parents of preterm and term-born infants within the broader causal framework.

Validity of a Delphi consensus definition of growth restriction in the newborn for identifying neonatal morbidity

BACKGROUND

Small for gestational age is defined as a birthweight below a birthweight percentile threshold, usually the 10th percentile, with the third or fifth percentile used to identify severe small for gestational age. Small for gestational age is used as a proxy for growth restriction in the newborn, but small-for-gestational-age newborns can be physiologically small and healthy. In addition, this definition excludes growth-restricted newborns who have weights more than the 10th percentile. To address these limits, a Delphi study developed a new consensus definition of growth restriction in newborns on the basis of neonatal anthropometric and clinical parameters, but it has not been evaluated.

OBJECTIVE

To assess the prevalence of growth restriction in the newborn according to the Delphi consensus definition and to investigate associated morbidity risks compared with definitions of Small for gestational age using birthweight percentile thresholds.

STUDY DESIGN

Data come from the 2016 and 2021 French National Perinatal Surveys, which include all births ≥22 weeks and/or with birthweights ≥500 g in all maternity units in France over 1 week. Data are collected from medical records and interviews with mothers after the delivery. The study population included 23,897 liveborn singleton births. The Delphi consensus definition of growth restriction was birthweight less than third percentile or at least 3 of the following criteria: birthweight, head circumference or length <10th percentile, antenatal diagnosis of growth restriction, or maternal hypertension. A composite of neonatal morbidity at birth, defined as 5-minute Apgar score <7, cord arterial pH <7.10, resuscitation and/or neonatal admission, was compared using the Delphi definition and usual birthweight percentile thresholds for defining small for gestational age using the following birthweight percentile groups: less than a third, third to fourth, and fifth to ninth percentiles. Relative risks were adjusted for maternal characteristics (age, parity, body mass index, smoking, educational level, preexisting hypertension and diabetes, and study year) and then for the consensus definition and birthweight percentile groups. Multiple imputation by chained equations was used to impute missing data. Analyses were carried out in the overall sample and among term and preterm newborns separately.

RESULTS

We identified that 4.9% (95% confidence intervals, 4.6-5.2) of newborns had growth restriction. Of these infants, 29.7% experienced morbidity, yielding an adjusted relative risk of 2.5 (95% confidence intervals, 2.2-2.7) compared with newborns without growth restriction. Compared with birthweight ≥10th percentile, morbidity risks were higher for low birthweight percentiles (less than third percentile: adjusted relative risk, 3.3 [95% confidence intervals, 3.0-3.7]; third to fourth percentile: relative risk, 1.4 [95% confidence intervals, 1.1-1.7]; fifth to ninth percentile: relative risk, 1.4 [95% confidence intervals, 1.2-1.6]). In adjusted models including the definition of growth restriction and birthweight percentile groups and excluding birthweights less than third percentile, which are included in both definitions, morbidity risks remained higher for birthweights at the third to fourth percentile (adjusted relative risk, 1.4 [95% confidence intervals, 1.1-1.7]) and fifth to ninth percentile (adjusted relative risk, 1.4 [95% confidence intervals, 1.2-1.6]), but not for the Delphi definition of growth restriction (adjusted relative risk, 0.9 [95% confidence intervals, 0.7-1.2]). Similar patterns were found for term and preterm newborns.

CONCLUSION

The Delphi consensus definition of growth restriction did not identify more newborns with morbidity than definitions of small for gestational age on the basis of birthweight percentiles. These findings illustrate the importance of evaluating the results of Delphi consensus studies before their adoption in clinical practice.

Fetal growth analysis from ultrasound videos based on different biometrics using optimal segmentation and hybrid classifier

Birth defects and their associated deaths, high health and financial costs of maternal care and associated morbidity are major contributors to infant mortality. If permitted by law, prenatal diagnosis allows for intrauterine care, more complicated hospital deliveries, and termination of pregnancy. During pregnancy, a set of measurements is commonly used to monitor the fetal health, including fetal head circumference, crown-rump length, abdominal circumference, and femur length. Because of the intricate interactions between the biological tissues and the US waves mother and fetus, analyzing fetal US images from a specialized perspective is difficult. Artifacts include acoustic shadows, speckle noise, motion blur, and missing borders. The fetus moves quickly, body structures close, and the weeks of pregnancy vary greatly. In this work, we propose a fetal growth analysis through US image of head circumference biometry using optimal segmentation and hybrid classifier. First, we introduce a hybrid whale with oppositional fruit fly optimization (WOFF) algorithm for optimal segmentation of segment fetal head which improves the detection accuracy. Next, an improved U-Net design is utilized for the hidden feature (head circumference biometry) extraction which extracts features from the segmented extraction. Then, we design a modified Boosting arithmetic optimization (MBAO) algorithm for feature optimization to selects optimal best features among multiple features for the reduction of data dimensionality issues. Furthermore, a hybrid deep learning technique called bi-directional LSTM with convolutional neural network (B-LSTM-CNN) for fetal growth analysis to compute the fetus growth and health. Finally, we validate our proposed method through the open benchmark datasets are HC18 (Ultrasound image) and oxford university research archive (ORA-data) (Ultrasound video frames). We compared the simulation results of our proposed algorithm with the existing state-of-art techniques in terms of various metrics.

Correlation between total deceleration area in CTG records and cord blood pH in pregnancies with IUGR

PURPOSE

Fetal cardiotocography is the most common method to assess fetal well-being during labor. Nevertheless, its predictive ability for acidemia is limited, both in low-risk and high-risk pregnancies (Nelson et al. in N Engl J Med 334: 613-9, 1996; Rinciples P et al. in Health and Human Development Workshop Report on Electronic Fetal Monitoring : Update on Definitions. no. 2007, 510-515, 2008), especially in high-risk pregnancies, such as those complicated by growth restriction. In this study we aim examine the association between deceleration and acceleration areas and other measure of fetal heart rate in intrapartum fetal monitoring and neonatal arterial cord blood pH in pregnancies complicated by growth restriction.

MATERIALS AND METHODS

A retrospective cohort study of 100 deliveries complicated by growth restriction, delivered during 2018, was conducted. Known major fetal anomalies, non-vertex presentation and elective cesarean deliveries were excluded. Total deceleration and acceleration areas were calculated as the sum of the areas within the deceleration and acceleration, respectively.

RESULTS

In deliveries complicated by growth restriction, cord blood pH is significantly associated with total deceleration area (p = 0.05) and correlates with cumulative duration of the decelerations (Spearman's rank -0.363, p < 0.05), and total acceleration area (-0.358, p < 0.05). By comparing the cord blood pH in deliveries with a total deceleration area that was above and below the median total deceleration area, we demonstrated a significant difference between the categories.

CONCLUSIONS

Cord blood pH significantly correlates with total deceleration area and other fetal monitoring characteristics in neonates with growth restriction. Future studies using real-time, machine-learning based techniques of fetal heart rate monitoring, may provide population specific threshold values that will support bedside clinical decision making and perhaps achieve better outcomes.

Leveraging the placenta to advance neonatal care

The impact of placental dysfunction and placental injury on the fetus and newborn infant has become a topic of growing interest in neonatal disease research. However, the use of placental pathology in directing or influencing neonatal clinical management continues to be limited for a wide range of reasons, some of which are historical and thus easily overcome today. In this review, we summarize the most recent literature linking placental function to neonatal outcomes, focusing on clinical placental pathology findings and the most common neonatal diagnoses that have been associated with placental dysfunction. We discuss how recent technological advances in neonatal and perinatal medicine may allow us to make a paradigm shift, in which valuable information provided by the placenta could be used to guide neonatal management more effectively, and to ultimately enhance neonatal care in order to improve our patient outcomes. We propose new avenues of clinical management in which the placenta could serve as a diagnostic tool toward more personalized neonatal intensive care unit management.

Inferring fetal growth restriction as rare, severe, and stable over time

Reduced birthweight is a marker of pathologies that impair growth and also decrease survival. However, "fetal growth restriction" remains poorly defined. Assuming that birthweight itself has no causal effect on neonatal mortality, we can estimate the features of pathological fetal growth that would be required to produce the observed pattern of weight-specific mortality. Under the simplest possible scenario, we find that at 39-41 weeks, pathological fetal growth restriction affects only about 0.5% of U.S. births, with a neonatal mortality risk up to 220-fold. This surprising concentration of pathology among a tiny subset of babies would account for roughly half of neonatal deaths at term. Moreover, the prevalence of these pathological births appears to have remained relatively stable over recent decades, even as neonatal mortality in the U.S. has declined by 90%. In our model, the decline has been driven by the reduction in baseline mortality (i.e., mortality among babies unaffected by growth pathologies), while the relative risk of death among pathologically grown infants has apparently remained stable. Fetal growth restriction is conventionally regarded as common and preventable. In contrast, our observations suggest that pathological fetal growth is rare and constant over time, perhaps the result of unpreventable stochastic errors in embryonic development. Public health strategies may be more effective by setting aside attempts to increase birthweight, and focusing instead on the discovery and support of factors (unrelated to birthweight) that have produced the striking reductions in neonatal mortality over time.

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

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.

Fetal growth trajectories of babies born large-for-gestational age in the LIFECODES Fetal Growth Study

BACKGROUND

Babies born large-for-gestational age have an increased risk of adverse health outcomes, including birth injuries, childhood obesity, and cardiometabolic disorders. However, little work has been done to characterize patterns of fetal growth among large-for-gestational age births, which may further elucidate high- and low-risk subgroups.

OBJECTIVE

This study aimed to identify subgroups of large-for-gestational age births based on trajectories of fetal growth derived from prenatal ultrasound measurements and explore differences in sociodemographic, pregnancy, and birth outcome characteristics across subgroups.

STUDY DESIGN

This study identified and described trajectories of fetal growth among large-for-gestational age births (n=235) in the LIFECODES Fetal Growth Study. Ultrasound measurements of fetal growth in middle to late pregnancy were abstracted from health records. Group-based multi-trajectory modeling was applied to measurements of head circumference, abdominal circumference, and femur length z-scores to identify multivariate trajectories of fetal growth. Moreover, sociodemographic variables, pregnancy characteristics, and birth outcomes based on trajectory membership were summarized.

RESULTS

This study identified 4 multivariate trajectories of fetal growth among large-for-gestational age births: catch-up growth (n=28), proportional abdominal circumference-to-femur length growth (n=67), disproportional abdominal circumference-to-femur length growth (n=96), and consistently large (n=44). Fetuses in the "catch-up growth" group exhibited small relative sizes in midpregnancy (ie, below average head circumference, abdominal circumference, and femur length z-scores) and large relative sizes in late pregnancy. Growth among these births was driven by increases in relative abdominal circumference and head circumference sizes. Participants who delivered births assigned to this group were less likely to have normal glucose control (40% vs 65%-75%) and more likely to have pregestational diabetes mellitus (36% vs 10%-17%) than other large-for-gestational age subgroups. In addition, the babies in this trajectory group were more likely to have macrosomia (86% vs 67%-73%) and to be admitted to the neonatal intensive care unit (32% vs 14%-21%) than other large-for-gestational age subgroups. In contrast, babies in the "consistently large" group had the largest relative size for all growth parameters throughout gestation and experienced a lower risk of adverse birth outcomes than other large-for-gestational age subgroups.

CONCLUSION

This study characterized several trajectories of fetal growth among large-for-gestational age births, which were related to different pregnancy characteristics and the distribution of adverse birth outcomes. Although the number of individuals within some trajectories was small, a subgroup that exhibited a catch-up growth phenotype during gestation was identified, which may be uniquely associated with exposure to pregestational diabetes mellitus and a higher risk of admission to the neonatal intensive care unit. These results have highlighted that the risk of adverse outcomes may not be evenly distributed across all large-for-gestational age births.

First year metabolic and hormonal behaviour define two different populations of SGA newborn for weight or height

Context

Small for gestational age (SGA) children have a particular metabolic and hormonal pattern at birth that change rapidly.

Objective

To evaluate the linear and weight growth in the first year of life in SGA children.

Design

Prospective, monocentric cohort study.

Setting

Real-world data collected from April 2012 to January 2016.

Patients

SGA newborns uniformly defined by either growth or length lower than -2 standard deviation for gestational age.

Interventions

All children were evaluated for one year after birth, at three days of life, then three, six and 12 months after birth.

Main outcome measures

Anthropometric parameters and biochemical variables, such as blood glucose, insulin, leptin, insulin-like growth factor (IGF)-1, IGF binding protein (IGFBP)3, and HOMA index.

Results

One hundred and thirty-three SGA children were enrolled. Length significantly improved one month after birth, whereas weight significantly increased only three months after birth. Biochemical variables increased during the first year of life, showing a prediction by IGFBP-3 and HOMA-index. Then, the casuistry was divided considering either weight, length or both, showing a different incidence. The biochemical variables changes recorded in the first step were maintained considering SGA children for weight or length, whereas they disappeared when weight and length were considered together.

Conclusions

Our study shows a specific catch-up growth for weight and length in SGA children. Moreover, we highlight that weight and length should be considered as independent parameters in SGA children, defining two different metabolic-hormonal populations with different conceivable predictive role in early catch-up growth and in later growth and metabolic status.

The limits of small-for-gestational-age as a high-risk category

SGA (small for gestational age) is widely used to identify high-risk infants, although with inconsistent definitions. Cut points range from 2.5th to 10th percentile of birthweight-for-gestational age. We used receiver operator characteristic curves (ROC) to identify the cut point with maximum sensitivity and specificity (Youden Index), and the area under the curve (AUC), which assesses overall discriminating power. Analysis was conducted on 3,836,034 US births (2015) and 292,279 Norwegian births (2010-14). Birthweight percentiles were calculated using exact birthweights at each week of gestational age, and then summarized across gestational ages. We also conducted a companion analysis of gestational age itself to consider the predictive power of gestational week of birth. Outcomes were neonatal mortality and cerebral palsy, both strongly associated with birthweight. Birthweight percentiles performed poorly in all analyses. The AUC for birthweight percentiles as a discriminator of neonatal mortality was 60% (where 50% is no better than a coin-toss). At such low discrimination, the Youden Index provides no useful SGA cut point. Results in Norway were virtually identical, with an AUC of 58%. The AUC with cerebral palsy as the outcome was even lower, at 54%. In contrast, gestational age had an AUC of 85% as a predictor of neonatal mortality, with < 37 weeks as the optimum cut point. SGA provides surprisingly poor identification of at-risk infants, while gestational age performs well. Similar results in two countries that differ in mean birthweight, percent preterm, and neonatal mortality suggest robustness across populations.