Large-for-gestational age diagnosed during second-trimester anatomy ultrasound and association with gestational diabetes and large-for-gestational age at birth

Risk of adverse pregnancy outcomes in pregnant women with gestational diabetes mellitus by age: a multicentric cohort study in Hebei, China

Gestational diabetes mellitus (GDM) is an unique metabolic disorder that occurs during pregnancy. Both GDM and advanced age increase the risk of adverse pregnancy outcomes. This study used a GDM cohort study to investigate the role of age in the adverse pregnancy outcomes for pregnant women with GDM. From 2015 to 2021, 308,175 pregnant women were selected, and the data received from 22 hospitals by the Hebei Province Maternal Near Miss Surveillance System. There were 24,551 pregnant women with GDM that were divided into five groups by age (20-24, 25-29, 30-34, 35-39, 40-44 years old). Because the prevalence of adverse pregnancy outcomes was lower in pregnant women with GDM aged 25-29, they were used as a reference group (P < 0.05). Compared with GDM women aged 25-29 years, GDM women aged 35-44 years had a significant higher risk of cesarean delivery (aOR: 2.86, 95% CI 2.52-3.25) (P < 0.001), abnormal fetal position (aOR: 1.78, 95% CI 1.31-2.37) (P < 0.001), pre-eclampsia (aOR: 1.28, 95% CI 1.01-1.61) (P < 0.05), macrosomia (aOR: 1.25, 95% CI 1.08-1.45) (P < 0.05), and large for gestational age (LGA) (aOR: 1.16, 95% CI 1.02-1.31) (P < 0.05), GDM women aged 40-44 years had a higher risk of placenta previa (aOR: 2.53, 95% CI 1.01-6.35) (P < 0.05), anemia (aOR: 3.45, 95% CI 1.23-9.68) (P < 0.05) and small for gestational age (aOR: 1.32, 95% CI 1.01-1.60) (P < 0.05). Advanced maternal age was an independent risk factor for abnormal fetal position, pre-eclampsia, anemia, macrosomia, and LGA in pregnant women with GDM.

Development and validation of risk prediction models for large for gestational age infants using logistic regression and two machine learning algorithms

BACKGROUND

Large for gestational age (LGA) is one of the adverse outcomes during pregnancy that endangers the life and health of mothers and offspring. We aimed to establish prediction models for LGA at late pregnancy.

METHODS

Data were obtained from an established Chinese pregnant women cohort of 1285 pregnant women. LGA was diagnosed as >90th percentile of birth weight distribution of Chinese corresponding to gestational age of the same-sex newborns. Women with gestational diabetes mellitus (GDM) were classified into three subtypes according to the indexes of insulin sensitivity and insulin secretion. Models were established by logistic regression and decision tree/random forest algorithms, and validated by the data.

RESULTS

A total of 139 newborns were diagnosed as LGA after birth. The area under the curve (AUC) for the training set is 0.760 (95% confidence interval [CI] 0.706-0.815), and 0.748 (95% CI 0.659-0.837) for the internal validation set of the logistic regression model, which consisted of eight commonly used clinical indicators (including lipid profile) and GDM subtypes. For the prediction models established by the two machine learning algorithms, which included all the variables, the training set and the internal validation set had AUCs of 0.813 (95% CI 0.786-0.839) and 0.779 (95% CI 0.735-0.824) for the decision tree model, and 0.854 (95% CI 0.831-0.877) and 0.808 (95% CI 0.766-0.850) for the random forest model.

CONCLUSION

We established and validated three LGA risk prediction models to screen out the pregnant women with high risk of LGA at the early stage of the third trimester, which showed good prediction power and could guide early prevention strategies.

Percent error of ultrasound examination to estimate fetal weight at term in different categories of birth weight with focus on maternal diabetes and obesity

BACKGROUND

Sonography based estimate of fetal weight is a considerable issue for delivery planning. The study evaluated the influence of diabetes, obesity, excess weight gain, fetal and neonatal anthropometrics on accuracy of estimated fetal weight with respect to the extent of the percent error of estimated fetal weight to birth weight for different categories.

METHODS

Multicenter retrospective analysis from 11,049 term deliveries and fetal ultrasound biometry performed within 14 days to delivery. Estimated fetal weight was calculated by Hadlock IV. Percent error from birth weight was determined for categories in 250 g increments between 2500 g and 4500 g. Estimated fetal weight accuracy was categorized as accurate ≤ 10% of birth weight, under- and overestimated by >  ± 10% - ± 20% and > 20%.

RESULTS

Diabetes was diagnosed in 12.5%, obesity in 12.6% and weight gain exceeding IOM recommendation in 49.1% of the women. The percentage of accurate estimated fetal weight was not significantly different in the presence of maternal diabetes (70.0% vs. 71.8%, p = 0.17), obesity (69.6% vs. 71.9%, p = 0.08) or excess weight gain (71.2% vs. 72%, p = 0.352) but of preexisting diabetes (61.1% vs. 71.7%; p = 0.007) that was associated with the highest macrosomia rate (26.9%). Mean percent error of estimated fetal weight from birth weight was 2.39% ± 9.13%. The extent of percent error varied with birth weight with the lowest numbers for 3000 g-3249 g and increasing with the extent of birth weight variation: 5% ± 11% overestimation in the lowest and 12% ± 8% underestimation in the highest ranges.

CONCLUSION

Diabetes, obesity and excess weight gain are not necessarily confounders of estimated fetal weight accuracy. Percent error of estimated fetal weight is closely related to birth weight with clinically relevant over- and underestimation at both extremes. This work provides detailed data regarding the extent of percent error for different birth weight categories and may therefore improve delivery planning.

Gestational Diabetes and Risk Assessment of Adverse Perinatal Outcomes and Newborns Early Motoric Development

Background and Objectives: The aim of this study was to analyze the presence of gestational diabetes mellitus (GDM) on maternal and fetal perinatal parameters, as well to evaluate the influence of GDM on neonatal early motoric development. Materials and Methods: In this prospective study, we evaluated 203 eligible participants that were admitted to obstetrics department for a labor. GDM was assessed by evaluation of maternal parameters, fetal parameters, as well its impact on infants early motoric development (Alberta Infant Motor Scale—AIMS). Results: Presence of GDM was significantly positively associated with: pre-pregnancy weight, obesity degree, weight at delivery, gestational weight gain (GWG), body mass index (BMI) at delivery, GWG and increased pre-pregnancy BMI, glucose levels in mother’s venous blood after the delivery, positive family history for cardiovascular disease, pregnancy-related hypertension, congenital thrombophilia, drug use in pregnancy, large for gestational age (LGA), mode of delivery (Cesarean section and instrumental delivery). Likewise, GDM association was detected for tested ultrasound parameters (biparietal diameter (BPD), head circumference (HC), abdominal circumference (AC), femoral length (FL)), length at birth, birth weight, newborn’s head circumference, newborn’s chest circumference, AIMS supination and pronation at three months, AIMS supination, pronation, sitting and standing at six months. Only Amniotic Fluid Index and AIMS supination at three months of infant’s age remained significantly associated in multivariate regression model. Conclusions: The presence of significant positive association of numerous tested parameters in our study on perinatal outcomes and early motoric development, points to the necessity of establishing appropriate clinical decision-making strategies for all pregnant woman at risk and emphasize the importance of providing adequate glycaemia control options and further regular follow ups during the pregnancy.