Ultrasound estimation of fetal weight: A formula for a Pakistani population

Development of birth weight estimation model for Ethiopian population from sonographic evaluation

BACKGROUND

Fetal birth weight (FBW) estimation involves predicting the weight of a fetus prior to delivery. This prediction serves as a crucial input for ensuring effective, accurate, and appropriate obstetric planning, management, and decision-making. Typically, there are two methods used to estimate FBW: the clinical method (which involves measuring fundal height and performing abdominal palpation) or sonographic evaluation. The accuracy of clinical method estimation relies heavily on the experience of the clinician. Sonographic evaluation involves utilizing various mathematical models to estimate FBW, primarily relying on fetal biometry. However, these models often demonstrate estimation errors that exceed acceptable levels, which can result in inadequate labor and delivery management planning. One source of this estimation error is sociodemographic variations between population groups in different countries. Additionally, inter- and intra-observer variability during fetal biometry measurement also contributes to errors in FBW estimation.

METHODS

In this research, a novel mathematical model was proposed through multiple regression analysis to predict FBW with an accepted level of estimation error. To develop the model, population data consisting of fetal biometry, fetal ultrasound images, obstetric variables, and maternal sociodemographic factors (age, marital status, ethnicity, educational status, occupational status, income, etc.) of the mother were collected. Two approaches were used to develop the mathematical model. The first method was based on fetal biometry data measured by a physician and the second used fetal biometry data measured using an image processing algorithm. The image processing algorithm comprises preprocessing, segmentation, feature extraction, and fetal biometry measurement.

RESULTS

The model developed using the two approaches were tested to assess their performance in estimating FBW, and they achieved mean percentage errors of 7.53% and 5.89%, respectively. Based on these results, the second model was chosen as the final model.

CONCLUSION

The findings indicate that the developed model can estimate FBW with an acceptable level of error for the Ethiopian population. Furthermore, this model outperforms existing models for FBW estimation. The proposed approach has the potential to reduce infant and maternal mortality rates by providing accurate fetal birth weight estimates for informed obstetric planning.

Ultrasonographic estimation of fetal weight: development of new model and assessment of performance of previous models

OBJECTIVES

To develop a new formula for ultrasonographic estimation of fetal weight and evaluate the accuracy of this and all previous formulae in the prediction of birth weight.

METHODS

The study population consisted of 5163 singleton pregnancies with fetal biometry at 22-43 weeks' gestation and live birth of a phenotypically normal neonate within 2 days of the ultrasound examination. Multivariable fractional polynomial analysis was used to determine the combination of variables that provided the best-fitting models for estimated fetal weight (EFW). A systematic review was also carried out of articles reporting formulae for EFW and comparing EFW to actual birth weight. The accuracy of each model for EFW was assessed by comparing mean percentage error, absolute mean error (AE), proportion of pregnancies with AE ≤ 10% and Euclidean distance.

RESULTS

The most accurate models, with the lowest Euclidean distance and highest proportion of AE ≤ 10%, were provided by the formulae incorporating ≥ 3 rather than < 3 biometrical measurements. The systematic review identified 45 studies describing a total of 70 models for EFW by various combinations of measurements of fetal head circumference (HC), biparietal diameter, femur length (FL) and abdominal circumference (AC). The most accurate model with the lowest Euclidean distance and highest proportion of AE ≤ 10% was provided by the formula of Hadlock et al., published in 1985, which incorporated measurements of HC, AC and FL; there was a highly significant linear association between EFW and birth weight (r = 0.959; P < 0.0001), and EFW was within 10% of birth weight in 80% of cases. The performance of the best model developed in this study, utilizing HC, AC and FL, was very similar to that of Hadlock et al. CONCLUSION: Despite many efforts to develop new models for EFW, the one reported in 1985 by Hadlock et al., from measurements of HC, AC and FL, provides the most accurate prediction of birth weight and can be used for assessment of all babies, including those suspected to be either small or large. Copyright © 2018 ISUOG. Published by John Wiley & Sons Ltd.

Ultrasonography-based Fetal Weight Estimation: Finding an Appropriate Model for an Indian Population

Background:

Very limited information is available regarding the accuracy and applicability of various ultrasonography parameters [abdominal circumference (AC), biparietal diameter (BPD), femur length (FL), and head circumference (HC)]-based fetal weight estimation models for Indian population. The objective of this study was to systematically evaluate commonly used fetal weight estimation models to determine their appropriateness for an Indian population.

Methods:

Retrospective data of 300 pregnant women was collected from a tertiary care center in Bengaluru, India. The inclusion criteria were a live singleton pregnancy, gestational age ≥ 34 weeks, and last ultrasound scan to delivery duration ≤ 7 days. Cases with suspected fetal growth restriction or malformation were excluded. For each case, fetal weight was estimated using 34 different models. The models specifically designed for low birth weight, small for gestation age, or macrosomic babies were excluded. The models were ranked based on their mean percentage error (MPE) and its standard deviation (random error). A model with the least MPE and random error ranking was considered as the best model.

Results:

In total, 149 cases were found suitable for the study. Out of 34, only 12 models had MPE within ± 10% and only seven models had random error < 10%. Most of the Western population-based models had a tendency to overestimate the fetal weight. Based on MPE and random error ranking, the Woo’s (AC-BPD) model was found to be the best, followed by Jordaan (AC), Combs (AC-HC-FL), Hadlock (AC-HC), and Hadlock-3 (AC-HC-FL) models. It was observed that the models based on just AC and AC-BPD combinations had statistically significant lesser MPE than the models based on all other combinations (p < 0.05).

Conclusion:

It was observed that the existing models have higher errors on Indian population than on their native populations. This points toward limitations in direct application of these models on Indian population without due consideration. Therefore, it is recommended that clinicians should exert caution in interpretation of fetal weight estimations based on these models. Moreover, this study highlights a need of models based on native Indian population.

A modified prenatal growth assessment score for the evaluation of fetal growth in the third trimester using single and composite biometric parameters

OBJECTIVE

To define modified Prenatal Growth Assessment Scores (mPGAS) for single and composite biometric parameters and determine their reference ranges in normal fetuses.

METHODS

Nine anatomical parameters (ap) were measured and the weight estimated (EWTa, EWTb) in a longitudinal study of 119 fetuses with normal neonatal growth outcomes. Expected third trimester size trajectories, obtained from second trimester Rossavik size models, were used in calculating Percent Deviations (% Dev's) and their age-specific reference ranges in each fetus. The components of individual % Dev's values outside their reference ranges, designated +iapPGAS, -iapPGAS, were averaged to give +apPGAS and -apPGAS values for the 3rd trimester. The +iapPGAS and -iapPGAS values for different combinations of ap (c1a (HC, AC, FDL, ThC, EWTa), c1b (HC, AC, FDL, ThC, EWTb), c2 (ThC, ArmC, AVol, TVol), c3 (HC, AC, FDL, EWTa)) were then averaged to give +icPGAS and -icPGAS values at different time points or at the end of the third trimester (+cPGAS, -cPGAS). Values for iapPGAS, ic1bPGAS, and ic2PGAS were compared to their respective apPGAS or cPGAS reference ranges.

RESULTS

All mPGAS values had one 95% range boundary at 0.0%. Upper boundaries of 1D +apPGAS values ranged from 0.0% (HC) to +0.49% (ThC) and were +0.06%, +2.3% and +1.8% for EWT, AVol and TVol, respectively. Comparable values for -apPGAS were 0.0% (BPD, FDL, HDL), to -0.58% (ArmC), -0.13% (EWT), -0.8% (AVol), and 0.0% (TVol). The +cPGAS, 95% reference range upper boundaries varied from +0.36% (c1b) to +0.89% (c2). Comparable values for -cPGAS lower boundaries were -0.17% (c1b) to -0.43% (c2).

CONCLUSIONS

The original PGAS concept has now been extended to individual biometric parameters and their combinations. With the standards provided, mPGAS values can now be tested to see if detection of different types of third trimester growth problems is improved.