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- Subjects and Methods
It has been suggested previously that differences exist in the accuracy of sonographic weight estimation between male and female fetuses1, and that these differences may be the result of sex-specific intrauterine growth patterns2–8, including sex-related differences in body composition and percent of body fat6, and in ratios among various biometric indices8. Indeed, we have recently reported that sonographic weight estimation was consistently more accurate for male than female fetuses, independent of the model used9.
It may be reasonable to hypothesize that the use of two distinct sex-specific models, optimized for male and female fetuses, may overcome this limitation. However, the impact of such sex-specific models on the accuracy of fetal weight estimation is as yet unclear. Surprisingly, none of the widely accepted sonographic models for fetal weight estimation10–15 includes fetal sex in the equation, and the results of only one study indicate that the use of such a sex-specific model may result in more accurate estimation than several widely used models7, 16, 17. Moreover, it is unknown whether such sex-related model optimization merely reflects a different set of sex-specific model coefficients or whether these sex-specific models also differ in the combination of biometric indices incorporated into the model.
Thus, the aim of the present study was to determine whether the use of a sex-specific sonographic model improves the accuracy or precision of fetal weight estimation, and to provide a better understanding of the reasons for sex-related differences in the accuracy of fetal weight estimation.
- Top of page
- Subjects and Methods
In the present study we sought to determine whether the use of a sex-specific sonographic model improves the accuracy of fetal weight estimation, as well as to provide a better understanding of the reasons for sex-related differences in the accuracy of fetal weight estimation. Our study revealed several key findings: 1) unadjusted published models are associated with the highest systematic error which is significantly higher for female than male fetuses; 2) adjustment of model coefficients to the local population decreases systematic error and results in a systematic error of similar magnitude but opposite in direction for male and female fetuses; 3) sex-specific (adjusted or newly developed) models are associated with the lowest systematic error and are the only models for which systematic error is statistically insignificant and similar for male and female fetuses (the latter observation being independent of birth-weight subgroup); and 4) random error (a measure of precision rather than accuracy) is unrelated to the type of model and to fetal sex.
Considering the sex-related differences in intrauterine growth patterns2–8 and in accuracy of sonographic weight estimation7, 9, it is reasonable to assume that the use of two distinct sex-specific models which are optimized for male and female fetuses will improve the accuracy of fetal weight estimation. However, this hypothesis has been tested in only a small number of studies7, 16, 17. Schild et al.7 developed a sex-specific model based on the results of 527 sonographic weight estimations which was subsequently found to be associated with the lowest mean absolute percentage error (6.8%) and the second best systematic error (−0.5 ± 8.6) when compared with 10 widely accepted sex-independent models17. Similarly, in the current study, using a large cohort of unselected women who underwent sonographic evaluation in a single tertiary center within 3 days prior to delivery, we were able to confirm our hypothesis and to demonstrate that sex-specific (adjusted or newly developed) models are associated with the lowest systematic error and are the only models that eliminate the sex-related differences in systematic error.
One possible explanation for the apparently higher accuracy of the newly developed sex-specific models may actually be unrelated to sex-specificity but rather to the fact that these new models were derived from the same population that was subsequently used in the evaluation phase, a potential bias that has not been addressed in previous studies in which sex-specific models were developed1, 17. In order to control for this potential bias, we have compared the new sex-specific models to sex-independent models that have been developed from the same population, as well as to modified versions of the Hadlock models I and II for which coefficients were adjusted to the study population, as has been previously described by Lee et al.25. Indeed, these control models demonstrated that adjustment of model coefficients to the local population significantly improves the accuracy of weight estimation. However, it was also clearly evident that the use of sex-specific models further improves the accuracy, independent of the adjustment of model coefficients to the local population.
It is unclear whether the higher accuracy achieved with sex-specific models is simply the result of a different set of model coefficients, adjusted to each of the sexes, or because the combination of biometric indices that provides the optimal fit to birth weight is different for males and females. The fact that there were no differences in systematic error between the sample-specific sex-specific versions of the Hadlock I and II models and the newly developed sex-specific models supports the former explanation, since the combination of biometric indices was identical for the male and female versions of the sample-specific sex-specific Hadlock I and II models. Nevertheless, in the process of development of the new models, we found that the optimal models for males and females differed with regard to the combination of fetal biometric indices (Table 1), although this conclusion may be limited by the potential inherent multicollinearity in these models (due to the fact that some of the indices and their transformations are highly correlated with each other).
The reason for the poor performance of Schild's sex-specific model in the current study is unclear. Possible explanations include: differences in the study population (c. 10% of the women in the study of Schild et al.1 had either gestational or pregestational diabetes and 30–40% delivered at < 37 weeks of gestation); the inclusion of weight estimations performed within up to 8 days of delivery (a considerable period during which significant fetal growth may occur); and the fact that sonographic examinations were performed by a large and heterogeneous group (18 residents and consultants)16. In addition, the fact that the performance of this model was evaluated using the same population from which it was generated and that the model was compared only to original, non-adjusted published models could have led to overestimation of its relative accuracy in the previous studies7, 16, 17.
With regard to the effect of birth-weight subgroup, we found that elimination of sex-related differences in the systematic error by using sex-specific models was observed in each of the different birth-weight subgroups. However, these sex-specific models were associated with highest accuracy (lowest systematic error) only in the larger interquartile birth-weight subgroup, while this advantage was not clearly observed in the extremes of birth weight (below the first quartile and above the third quartile). This observation is probably related to the general tendency for over- and underestimation of birth weight in cases of low and high birth weight, respectively18, as was observed also in the current study. These birth weight-related changes in systematic error shift the overall optimized systematic error achieved with sex-specific models towards a more positive or more negative systematic error, respectively. Clearly, an optimal customized model would need to account for multiple factors that are known to affect the accuracy of weight estimation, including fetal sex and birth-weight subgroup.
In conclusion, we have confirmed our hypothesis that the use of sex-specific models improves the accuracy of fetal weight estimation, principally because the optimal set of model coefficients differs for male and female fetuses. Improved accuracy is mainly the result of a decrease in systematic error, as random error was not affected by the use of such sex-specific models. Nevertheless, it should be emphasized that from a more clinical perspective, the differences in systematic errors were relatively small and thus did not translate into significant differences in other measures of accuracy which may be of more interest to the clinician (e.g. the proportion of estimations within 10% of birth weight). Still, although the absolute decrease in systematic error achieved with these sex-specific models is relatively small, the use of such models may be one of several steps that may eventually contribute to improvement of the accuracy of fetal weight estimation, including the use of alternative biometric indices25–28, incorporation of other clinical factors into the models29, 30 and the use of models optimized for specific weight ranges1, 31, 32.