# Predictive Accuracy of Ultrasound Biometry and Maternal Factors in Identifying Large-for-Gestational-Age Neonates at 30–34 Weeks

## TL;DR

This study compares models using ultrasound and maternal factors to predict large-for-gestational-age babies early in pregnancy.

## Contribution

The study introduces and evaluates new prediction models combining ultrasound biometry and maternal data for early LGA prediction.

## Key findings

- Ultrasound biometric models (AC, FL) and maternal factors showed similar predictive accuracy (AUC ~85%).
- Estimated fetal weight alone had lower accuracy (AUC 77.5%).
- Polyhydramnios and abdominal circumference were strong predictors of LGA.

## Abstract

Background/Objectives: To construct and compare multivariable prediction models for the early prediction of large-for-gestational-age (LGA) neonates, using ultrasound biometry and maternal characteristics. Methods: This retrospective cohort study analyzed data from singleton pregnancies that underwent routine ultrasound examinations at 30+0–34+0 weeks of gestation. Ultrasound parameters included fetal abdominal circumference (AC), head circumference (HC), femur length (FL), HC-to-AC ratio, mean uterine artery pulsatility index (mUtA-PI), and presence of polyhydramnios. LGA neonates were defined as those having a birthweight > 90th percentile. Logistic regression was used to evaluate associations between ultrasound markers and LGA after adjusting for the following maternal and pregnancy-related covariates: maternal age, body mass index, parity, gestational diabetes mellitus (GDM), pre-existing diabetes, previous cesarean section (PCS), assisted reproductive technology (ART) use, smoking, hypothyroidism, and chronic hypertension. Associations were expressed as adjusted odds ratios (aORs) with 95% confidence intervals (CIs). Three prognostic models were developed utilizing the following predictors: (i) biometric ultrasound measurements including AC, HC-to-AC ratio, FL, UtA-PI, and polyhydramnios (Model 1), (ii) a combination of biometric ultrasound measurements and clinical–maternal data (Model 2), and (iii) only the estimated fetal weight (EFW) (Model 3). Results: In total, 3808 singleton pregnancies were included in the analyses. The multivariable analysis revealed that AC (aOR 1.07, 95% CI [1.06, 1.08]), HC to AC (aOR 1.01, 95% CI [1.006, 1.01]), FL (aOR 1.01, 95% CI [1.009, 1.01]), and the presence of polyhydramnios (aOR 4.97, 95% CI [0.7, 58.8]) were associated with an increased risk of LGA, while a higher mUtA-PI was associated with a reduced risk (aOR 0.98, 95% CI [0.98, 0.99]). Maternal parameters, such as GDM, pre-existing diabetes, elevated pre-pregnancy BMI, absence of uterine artery notching, mUtA-PI, and multiparity, were significantly higher in the LGA group. Both models 1 and 2 showed similar performance (AUCs: 84.7% and 85.3%, respectively) and outperformed model 3 (AUC: 77.5%). Bootstrap and temporal validation indicated minimal overfitting and stable model performance, while decision curve analysis supported potential clinical utility. Conclusions: Models using biometric and Doppler ultrasound at 30–34 weeks demonstrated good discriminative ability for predicting LGA neonates, with an AUC up to 84.7%. Adding maternal characteristics did not significantly improve performance, while the biometric model performed better than EFW alone. Sensitivity at conventional thresholds was low but increased substantially when lower probability cut-offs were applied, illustrating the model’s threshold-dependent flexibility for early risk stratification in different clinical screening needs. Although decision curve analysis was performed to explore potential clinical utility, external validation and prospective assessment in clinical settings are still needed to confirm generalizability and to determine optimal decision thresholds for clinical application.