Use of ultrasound fetal shoulder soft tissue thickness measurement in estimation of fetal weight

Methodology Used in Studies Aimed at Measuring Fetal Soft Tissues by 2D Ultrasound for the Screening of Large for Gestational Age Fetuses: A Systematic Review

Management of suspected large for gestational age (LGA) fetuses remains unclear because ultrasound-estimated fetal weight (EFW) is not accurate. This was a systematic review of observational studies on fetal soft tissues measurements used alone or in combination to create a new EFW formula, to improve the screening for LGA fetuses. Studies were scored using a predefined set of independently agreed methodological criteria and an overall quality score was assigned for study design, statistical analysis, and reporting methods. There is a need to standardize methodologies for soft fetal tissue measurements. We propose a set of suggestions for this purpose.

A predictive model of macrosomic birth based upon real-world clinical data from pregnant women

BACKGROUND

Fetal macrosomia is associated with an increased risk of several maternal and newborn complications. Antenatal predication of fetal macrosomia remains challenging. We aimed to develop a nomogram model for the prediction of macrosomia using real-world clinical data to improve the sensitivity and specificity of macrosomia prediction.

METHODS

In the present study, we performed a retrospective, observational study based on 13,403 medical records of pregnant women who delivered singleton infants at a tertiary hospital in Shanghai from 1 January 2018 through 31 December 2019. We split the original dataset into a training set (n = 9382) and a validation set (n = 4021) at a 7:3 ratio to generate and validate our model. The candidate variables, including maternal characteristics, laboratory tests, and sonographic parameters were compared between the two groups. A univariate and multivariate logistic regression was carried out to explore the independent risk factors for macrosomia in pregnant women. Thus, the regression model was adopted to establish a nomogram to predict the risk of macrosomia. Nomogram performance was determined by discrimination and calibration metrics. All the statistical analysis was analyzed using R software.

RESULTS

We compared the differences between the macrosomic and non-macrosomic groups within the training set and found 16 independent risk factors for macrosomia (P < 0.05), including biparietal diameter (BPD), head circumference (HC), femur length (FL), amniotic fluid index (AFI) at the last prenatal examination, pre-pregnancy body mass index (BMI), and triglycerides (TG). Values for the areas under the curve (AUC) for the nomogram model were 0.917 (95% CI, 0.908-0.927) and 0.910 (95% CI, 0.894-0.927) in the training set and validation set, respectively. The internal and external validation of the nomogram demonstrated favorable calibration as well as discriminatory capability of the model.

CONCLUSIONS

Our model has precise discrimination and calibration capabilities, which can help clinical healthcare staff accurately predict macrosomia in pregnant women.