Cost-effectiveness of a gestational age metabolic algorithm for preterm and small-for-gestational-age classification

Accuracy of prenatal and postnatal biomarkers for estimating gestational age: a systematic review and meta-analysis

Background

Knowledge of gestational age (GA) is key in clinical management of individual obstetric patients, and critical to be able to calculate rates of preterm birth and small for GA at a population level. Currently, the gold standard for pregnancy dating is measurement of the fetal crown rump length at 11-14 weeks of gestation. However, this is not possible for women first presenting in later pregnancy, or in settings where routine ultrasound is not available. A reliable, cheap and easy to measure GA-dependent biomarker would provide an important breakthrough in estimating the age of pregnancy. Therefore, the aim of this study was to determine the accuracy of prenatal and postnatal biomarkers for estimating gestational age (GA).

Methods

Systematic review prospectively registered with PROSPERO (CRD42020167727) and reported in accordance with the PRISMA-DTA. Medline, Embase, CINAHL, LILACS, and other databases were searched from inception until September 2023 for cohort or cross-sectional studies that reported on the accuracy of prenatal and postnatal biomarkers for estimating GA. In addition, we searched Google Scholar and screened proceedings of relevant conferences and reference lists of identified studies and relevant reviews. There were no language or date restrictions. Pooled coefficients of correlation and root mean square error (RMSE, average deviation in weeks between the GA estimated by the biomarker and that estimated by the gold standard method) were calculated. The risk of bias in each included study was also assessed.

Findings

Thirty-nine studies fulfilled the inclusion criteria: 20 studies (2,050 women) assessed prenatal biomarkers (placental hormones, metabolomic profiles, proteomics, cell-free RNA transcripts, and exon-level gene expression), and 19 (1,738,652 newborns) assessed postnatal biomarkers (metabolomic profiles, DNA methylation profiles, and fetal haematological components). Among the prenatal biomarkers assessed, human chorionic gonadotrophin measured in maternal serum between 4 and 9 weeks of gestation showed the highest correlation with the reference standard GA, with a pooled coefficient of correlation of 0.88. Among the postnatal biomarkers assessed, metabolomic profiling from newborn blood spots provided the most accurate estimate of GA, with a pooled RMSE of 1.03 weeks across all GAs. It performed best for term infants with a slightly reduced accuracy for preterm or small for GA infants. The pooled RMSEs for metabolomic profiling and DNA methylation profile from cord blood samples were 1.57 and 1.60 weeks, respectively.

Interpretation

We identified no antenatal biomarkers that accurately predict GA over a wide window of pregnancy. Postnatally, metabolomic profiling from newborn blood spot provides an accurate estimate of GA, however, as this is known only after birth it is not useful to guide antenatal care. Further prenatal studies are needed to identify biomarkers that can be used in isolation, as part of a biomarker panel, or in combination with other clinical methods to narrow prediction intervals of GA estimation.

Funding

The research was funded by the Bill and Melinda Gates Foundation (INV-000368). ATP is supported by the Oxford Partnership Comprehensive Biomedical Research Centre with funding from the NIHR Biomedical Research Centre funding scheme. The views expressed are those of the authors and not necessarily those of the UK National Health Service, the NIHR, the Department of Health, or the Department of Biotechnology. The funders of this study had no role in study design, data collection, analysis or interpretation of the data, in writing the paper or the decision to submit for publication.

Association between gestational weight gain and preterm birth and post-term birth: a longitudinal study from the National Vital Statistics System database

BACKGROUND

To evaluate the association between gestational weight gain (GWG) and preterm birth and post-term birth.

METHODS

This longitudinal-based research studied singleton pregnant women from the National Vital Statistics System (NVSS) (2019). Total GWG (kg) was converted to gestational age-standardized z scores. The z-scores of GWG were divided into four categories according to the quartile of GWG, and the quantile 2 interval was used as the reference for the analysis. Univariate and multivariate logistic regression analyses were performed to investigate the association between GWG and preterm birth, post-term birth, and total adverse outcome (preterm birth + post-term birth). Subgroup analysis stratified by pre-pregnancy body mass index (BMI) was used to estimate associations between z-scores and outcomes.

RESULTS

Of the 3,100,122 women, preterm birth occurred in 9.45% (292,857) population, with post-term birth accounting for 4.54% (140,851). The results demonstrated that low GWG z-score [odds ratio (OR): 1.04, 95% confidence interval (CI): 1.03 to 1.05, P < 0.001], and higher GWG z-scores (quantile 3: OR: 1.42, 95% CI: 1.41 to 1.44, P < 0.001; quantile 4: OR: 2.79, 95% CI: 2.76 to 2.82, P < 0.001) were positively associated with preterm birth. Low GWG z-score (OR: 1.18, 95% CI: 1.16 to 1.19, P < 0.001) was positively associated with an increased risk of post-term birth. However, higher GWG z-scores (quantile 3: OR: 0.84, 95% CI: 0.83 to 0.85, P < 0.001; quantile 4: 0.59, 95% CI: 0.58 to 0.60, P < 0.001) was associated with a decreased risk of post-term birth. In addition, low GWG z-score and higher GWG z-scores were related to total adverse outcome. A subgroup analysis demonstrated that pre-pregnancy BMI, low GWG z-score was associated with a decreased risk of preterm birth among BMI-obesity women (OR: 0.96, 95% CI: 0.94 to 0.98, P < 0.001).

CONCLUSION

Our result suggests that the management of GWG may be an important strategy to reduce the number of preterm birth and post-term birth.

Effects of Yoga exercise on anxiety and fetus growth in pregnant women with small for gestational age fetus

OBJECTIVE

To investigate the effect of yoga on anxiety and fetal weight of pregnant women carrying fetus small for gestational age (SGA).

METHODS

In this retrospective analysis, a total of 186 pregnant women with SGA fetus in our hospital from January 2015 to December 2017 were enrolled in this study. Among them, 90 patients received routine check-up were included in the control group, and the other 96 patients who had professional yoga exercise were included in the observation group. The differences of anxiety scale scores and fetal weight between the two groups before and after intervention were compared.

RESULTS

There was no significant difference in scores of anxiety scale (SAS) between the two groups before intervention. After intervention, the SAS score of pregnant women in intervention group was (46.48±3.79) was significantly lower than that in control group (60.13±4.25). There was also significant difference in fetal growth trajectory between the two groups, with a significant increase of 1021.36 g in the intervention group compared with 795.62 g in the control group (P<0.05). Furthermore, single regression analysis showed that average gestational weeks (r=0.064. P=0.011), yoga exercise (r=0.043, P<0.001), forceps use (r=0.338, P<0.001) and conversion to cesarean section (r=0.431, P<0.001) showed a significant correlation with anxiety and fetus growth in pregnant women carrying SGA fetus. Multiple regression analysis showed that yoga exercise (P<0.001) was selected as independent variables in the multiple regression model of anxiety and fetus growth in pregnant women with SGA fetus.

CONCLUSION

Yoga can effectively reduce the anxiety of pregnant women with small gestational age fetus and good for the growth and development of the fetus.

Real world external validation of metabolic gestational age assessment in Kenya

Using data from Ontario Canada, we previously developed machine learning-based algorithms incorporating newborn screening metabolites to estimate gestational age (GA). The objective of this study was to evaluate the use of these algorithms in a population of infants born in Siaya county, Kenya. Cord and heel prick samples were collected from newborns in Kenya and metabolic analysis was carried out by Newborn Screening Ontario in Ottawa, Canada. Postnatal GA estimation models were developed with data from Ontario with multivariable linear regression using ELASTIC NET regularization. Model performance was evaluated by applying the models to the data collected from Kenya and comparing model-derived estimates of GA to reference estimates from early pregnancy ultrasound. Heel prick samples were collected from 1,039 newborns from Kenya. Of these, 8.9% were born preterm and 8.5% were small for GA. Cord blood samples were also collected from 1,012 newborns. In data from heel prick samples, our best-performing model estimated GA within 9.5 days overall of reference GA [mean absolute error (MAE) 1.35 (95% CI 1.27, 1.43)]. In preterm infants and those small for GA, MAE was 2.62 (2.28, 2.99) and 1.81 (1.57, 2.07) weeks, respectively. In data from cord blood, model accuracy slightly decreased overall (MAE 1.44 (95% CI 1.36, 1.53)). Accuracy was not impacted by maternal HIV status and improved when the dating ultrasound occurred between 9 and 13 weeks of gestation, in both heel prick and cord blood data (overall MAE 1.04 (95% CI 0.87, 1.22) and 1.08 (95% CI 0.90, 1.27), respectively). The accuracy of metabolic model based GA estimates in the Kenya cohort was lower compared to our previously published validation studies, however inconsistency in the timing of reference dating ultrasounds appears to have been a contributing factor to diminished model performance.

Metabolic gestational age assessment in low resource settings: a validation protocol

Preterm birth is the leading global cause of neonatal morbidity and mortality. Reliable gestational age estimates are useful for quantifying population burdens of preterm birth and informing allocation of resources to address the problem. However, evaluating gestational age in low-resource settings can be challenging, particularly in places where access to ultrasound is limited. Our group has developed an algorithm using newborn screening analyte values derived from dried blood spots from newborns born in Ontario, Canada for estimating gestational age within one to two weeks. The primary objective of this study is to validate a program that derives gestational age estimates from dried blood spot samples (heel-prick or cord blood) collected from health and demographic surveillance sites and population representative health facilities in low-resource settings in Zambia, Kenya, Bangladesh and Zimbabwe. We will also pilot the use of an algorithm to identify birth percentiles based on gestational age estimates and weight to identify small for gestational age infants. Once collected from local sites, samples will be tested by the Newborn Screening Ontario laboratory at the Children's Hospital of Eastern Ontario (CHEO) in Ottawa, Canada. Analyte values will be obtained through laboratory analysis for estimation of gestational age as well as screening for other diseases routinely conducted at Ontario's newborn screening program. For select conditions, abnormal screening results will be reported back to the sites in real time to facilitate counseling and future clinical management. We will determine the accuracy of our existing algorithm for estimation of gestational age in these newborn samples. Results from this research hold the potential to create a feasible method to assess gestational age at birth in low- and middle-income countries where reliable estimation may be otherwise unavailable.