Menstrual age-dependent systematic error in sonographic fetal weight estimation: a mathematical model

Birth weight prediction models for the different gestational age stages in a Chinese population

The study aims to develop new birth weight prediction models for different gestational age stages using 2-dimensional (2D) ultrasound measurements in a Chinese population. 2D ultrasound was examined in pregnant women with normal singleton within 3 days prior to delivery (28–42 weeks’ gestation). A total of 19,310 fetuses were included in the study and randomly split into the training group and the validation group. Gestational age was divided into five stages: 28–30, 31–33, 34–36, 37–39 and 40–42 weeks. Multiple linear regression (MLR), fractional polynomial regression (FPR) and volume-based model (VM) were used to develop birth weight prediction model. New staged prediction models (VM for 28–36 weeks, MLR for 37–39 weeks, and FPR for 40–42 weeks) provided lower systematic errors and random errors than previously published models for each gestational age stage in the training group. The similar results were observed in the validation group. Compared to the previously published models, new staged models had the lowest aggregate systematic error (0.31%) and at least a 19.35% decrease; at least a 4.67% decrease for the root-mean-square error (RMSE). The prediction rates within 5% and 10% of birth weight for new staged models were higher than those for previously published models, which were 54.47% and 85.10%, respectively. New staged birth weight prediction models could improve the accuracy of birth weight estimation for different gestational age stages in a Chinese population.

Statistical limits in sonographic estimation of birth weight

PURPOSE

The accuracy of sonographic estimation of birth weight (EBW) is compromised by the precision of the biometrical measurements and the quality of the algorithms. This prospective study was to evaluate technical aspects to derive new equations for the EBW.

METHODS

Three consecutive phases were carried out (1) to recruit a homogenous population, (2) to derive eight new algorithms using a multiple stepwise mathematical/statistical method, and (3) to test the accuracy of the developed equations. Only those patients with a singleton pregnancy who delivered within 48 h from the scan were considered for the analysis.

RESULTS

The study population was made of 473 women. Four polynomial, two square root and two logarithmic algorithms were derived from a balanced study group of 200 women selected from the original study population. These formulas were subsequently applied and compared between them and showed a significant correlation with birth weight (p < 0.0001) and satisfactory statistical performances (r > 0.9), nevertheless they performed similarly to other equations previously published.

CONCLUSIONS

The present findings define better the limitations associated with the intrinsic properties of algorithms and highlight that the possibility to improve the precision of sonographic measurements remains the only point at issue to increase the accuracy in the prediction of birth weight.

Accuracy of sonographic weight estimation as a function of fetal sex

OBJECTIVES

To determine whether the accuracy of sonographic fetal weight estimation is related to fetal sex.

METHODS

The accuracy of sonographic fetal weight estimation was compared between male and female fetuses using 3672 sonographic weight estimations performed within 3 days prior to delivery. Fetal weight was estimated using eight regression models that are based on different combinations of the following biometric parameters: abdominal circumference (AC), femur diaphysis length (FL), biparietal diameter (BPD) and head circumference (HC).

RESULTS

In seven out of the eight models tested, the presence of a male fetus was associated with a significantly lower systematic error compared with a female fetus (-0.2 to 2.1% vs. 1.3 to 6%, P<0.001). On multivariate analysis, fetal sex was independently associated with sonographic accuracy so that the likelihood of a weight estimation within 10% of birth weight was 30% higher for male fetuses compared with female fetuses. The biometric parameters that contributed most to these sex-related differences were FL and AC, while models that included HC were associated with the lowest differences in the systematic error between male fetuses and female fetuses. For most models, the random error and correlation between estimated weight and birth weight were not affected by fetal sex (8.1-12.8% vs. 8.2-13.6%, and 0.856-0.944 vs. 0.842-0.944, respectively).

CONCLUSION

Sonographic estimation of fetal weight is more accurate for male fetuses than for female fetuses. The use of sex-specific models may improve the accuracy of fetal weight estimation for female fetuses.

Analysis of factors influencing the ultrasonic fetal weight estimation

OBJECTIVE

The aim of our study was the evaluation of sonographic fetal weight estimation taking into consideration 9 of the most important factors of influence on the precision of the estimation.

METHODS

We analyzed 820 singleton pregnancies from 22 to 42 weeks of gestational age. We evaluated 9 different factors that potentially influence the precision of sonographic weight estimation (time interval between estimation and delivery, experts vs. less experienced investigator, fetal gender, gestational age, fetal weight, maternal BMI, amniotic fluid index, presentation of the fetus, location of the placenta). Finally, we compared the results of the fetal weight estimation of the fetuses with poor scanning conditions to those presenting good scanning conditions.

RESULTS

Of the 9 evaluated factors that may influence accuracy of fetal weight estimation, only a short interval between sonographic weight estimation and delivery (0-7 vs. 8-14 days) had a statistically significant impact.

CONCLUSION

Of all known factors of influence, only a time interval of more than 7 days between estimation and delivery had a negative impact on the estimation.

An informative probability model enhancing real time echobiometry to improve fetal weight estimation accuracy

A multinormal probability model is proposed to correct human errors in fetal echobiometry and improve the estimation of fetal weight (EFW). Model parameters were designed to depend on major pregnancy data and were estimated through feed-forward artificial neural networks (ANNs). Data from 4075 women in labour were used for training and testing ANNs. The model was implemented numerically to provide EFW together with probabilities of congruence among measured echobiometric parameters. It enabled ultrasound measurement errors to be real-time checked and corrected interactively. The software was useful for training medical staff and standardizing measurement procedures. It provided multiple statistical data on fetal morphometry and aid for clinical decisions. A clinical protocol for testing the system ability to detect measurement errors was conducted with 61 women in the last week of pregnancy. It led to decisive improvements in EFW accuracy.

Accuracy of sonographically estimated fetal weight in 840 women with different pregnancy complications prior to induction of labor

OBJECTIVES

To evaluate the accuracy of sonographically estimated fetal weight (EFW) shortly before induction of labor in the presence of different pregnancy complications, and to define possible variables affecting it.

METHODS

The study sample consisted of 840 women with singleton pregnancies and cephalic presentation who were admitted to our unit for induction of labor between January 1999 and December 2000. All underwent detailed ultrasound assessment for EFW, amniotic fluid index, biophysical profile and placental location. Indications included previous Cesarean section, postdate pregnancy, pregnancy-induced hypertension, diabetic pregnancy, suspected large-for-gestational age (LGA) infants, suspected fetal growth restriction (FGR), oligohydramnios, decreased fetal movements, premature rupture of membranes at or before term. EFW was calculated after measuring fetal abdominal circumference and femur length. The EFW was compared with the weight at delivery, 1-3 days later.

RESULTS

There was a high correlation between EFW and birth weight (R(2) = 0.775, P < 0.001). The mean birth weight was 3207 +/- 561 g, and mean absolute weight difference was 227 +/- 197 g; (absolute range, 0-1700 g; actual range, - 986 to + 1700 g). The mean weight difference was significantly different between the patients with LGA infants, FGR infants and preterm premature rupture of membranes (PPROM) (- 110 +/- 281 g, + 113 +/- 195 g and + 115 +/- 307 g, respectively, P < 0.01). Stepwise linear regression analysis of the effects of maternal and pregnancy characteristics on the weight difference yielded lower gestational age, higher birth weight, anterior placenta, higher gravidity, and younger maternal age as independent and significant variables associated with greater actual weight difference inaccuracy (R(2) = 0.099, P < 0.001), and higher birth weight as the only independent and significant variable associated with greater absolute weight difference (R(2) = 0.09, P = 0.018).

CONCLUSIONS

The sonographic EFW is highly correlated with birth weight. However, clinicians should be aware of the risk of overestimation in pregnancies with suspected LGA and underestimation in pregnancies with PPROM and suspected FGR.

A new growth chart for preterm babies: Babson and Benda's chart updated with recent data and a new format

BACKGROUND

The Babson and Benda 1976 "fetal-infant growth graph" for preterm infants is commonly used in neonatal intensive care. Its limits include the small sample size which provides low confidence in the extremes of the data, the 26 weeks start and the 500 gram graph increments. The purpose of this study was to develop an updated growth chart beginning at 22 weeks based on a meta-analysis of published reference studies.

METHODS

The literature was searched from 1980 to 2002 for more recent data to complete the pre and post term sections of the chart. Data were selected from population studies with large sample sizes. Comparisons were made between the new chart and the Babson and Benda graph. To validate the growth chart the growth results from the National Institute of Child Health and Human Development Neonatal Research Network (NICHD) were superimposed on the new chart.

RESULTS

The new data produced curves that generally followed patterns similar to the old growth graph. Mean differences between the curves of the two charts reached statistical significance after term. Babson's 10th percentiles fell between the new data percentiles: the 5th to 17th for weight, the 5th and 15th for head circumference, and the 6th and 16th for length. The growth patterns of the NICHD infants deviated away from the curves of the chart in the first weeks after birth. When the infants reached an average weight of 2 kilograms, those with a birthweight in the range of 700 to 1000 grams had achieved greater than the 10th percentile on average for head growth, but remained below the 3rd percentile for weight and length.

CONCLUSION

The updated growth chart allows a comparison of an infant's growth first with the fetus as early as 22 weeks and then with the term infant to 10 weeks. Comparison of the size of the NICHD infants at a weight of 2 kilograms provides evidence that on average preterm infants are growth retarded with respect to weight and length while their head size has caught up to birth percentiles. As with all meta-analyses, the validity of this growth chart is limited by the heterogeneity of the data sources. Further validation is needed to illustrate the growth patterns of preterm infants to older ages.