A Systematic Evaluation of Ultrasound-based Fetal Weight Estimation Models on Indian Population.

Background: The purpose of this study was to systematically evaluate ultrasound-based fetal weight estimation models on Indian population to find out their performance across different weight bands and ability to correctly categorize low birth weight (LBW) and high birth weight (HBW) fetuses.

Methods: We used retrospectively collected data of 154 cases for the study. Inclusion criteria were a live singleton pregnancy, gestational age ≥34 weeks and ultrasound scan to delivery duration ≤7 days. Cases with fetal growth restriction or malformation were excluded. The cases were divided into standard weight bands of 500 g each based on newborns' actual birth weights (ABW). For each weight band, performance of 12 different models based on abdominal circumference (AC), biparietal diameter (BPD), head circumference (HC) and femur length (FL) was evaluated by mean percentage error (MPE) and its standard deviation (random error). Sensitivity and positive predict value (PPV) of models to categorize LBW (ABW ≤ 2500 g) and HBW (ABW >3500 g) neonates were also evaluated.

Results: We observed a significant variation in MPE of the 12 models with no single model being consistently superior across all the weight bands. For the cases with birth weight ≤3000 g, the Woo (AC-BPD) model was found to be more appropriate, whereas for the cases with birth weight >3000 g the Woo (AC-BPD-FL) model was found more appropriate. In general, models had a tendency to overestimate fetal weight in LBW neonates and underestimate it in HBW neonates. Overall, the models showed poor sensitivity and PPV to categorize LBW and HBW neonates. The highest sensitivity (57.1%) for LBW identification was observed with the Woo (AC-BPD) model; the highest PPV (50%) for HBW neonate identification was observed with the Hadlock (AC-HC), Warsof (AC-BPD) and Combs (AC-HC-FL) model.

Conclusion: We found that the existing fetal weight estimation models have high systematic and random errors on Indian population, with a general tendency of overestimation of fetal weight in the LBW category and underestimation in the HBW category. We also observed that these models have a limited ability to predict babies at a risk of either low or high birth weight. It is recommended that the clinicians should consider all these factors, while interpreting estimated weight given by the existing models.