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CONTRIBUTION What are the novel findings of this work? This study examines the impact of measurement errors in fetal head circumference, abdominal circumference and femur length on sonographic estimated fetal weight (EFW) and their effect on the prediction of small‐ (SGA) and large‐ (LGA) for‐gestational‐age fetuses. What are the clinical implications of this work? Measurement error in fetal biometry causes substantial error in EFW, resulting in misclassification of SGA and LGA fetuses. The extent to which improvement can be achieved through effective quality assurance remains to be seen but, as a first step, it is important for practitioners to understand how biometric measurement error impacts on the prediction of SGA and LGA fetuses.

or in EFW, resulting in misclassification of SGA and LGA fetuses. The extent to which improvement can be achieved through effective quality assurance remains to be seen but, as a first step, it is important for practitioners to understand how biometric measurement error impacts on the prediction of SGA and LGA fetuses. INTRODUCTION Small‐for‐gestational‐age (SGA) neonates are at increased risk of stillbirth and adverse perinatal outcome1, 2, 3, 4. The expectation that these risks can potentially be reduced by medical interventions, such as early delivery, has led to the implementation of prenatal strategies for the identification of SGA fetuses. National guidelines from many developed countries define fetal growth restriction on the basis of ultrasonographic estimated fetal weight (EFW) < 10th percentile and provide recommendations on monitoring and criteria for delivery of such pregnancies5. About 85% of SGA neonates are born at term6 and there is now good evidence that the predictive performance for a term SGA neonate is higher if, first, the method of screening is routine third‐trimester ultrasonographic fetal biometry rather than selective ultrasonography based on maternal risk factors and serial measurements of symphysis–fundus height7, and, second, the routine scan is carried out at 35 + 0 to 36 + 6 weeks' gestation rather than at 31 + 0 to 33 + 6 weeks8, 9. Similarly, large‐for‐gestational‐age (LGA) neonates with birth weight > 90th percentile are at increased risk of perinatal death, birth injury and adverse neonatal outcome2, 4, 10, 11. Such risks could potentially be reduced by elective Cesarean section or early induction of labor to reduce the inevitable increase in fetal size with advancing gestational age12, 13, 14. As in cases of a SGA neonate, the best prediction of a LGA neonate is achieved by universal sonographic fetal biometry at 35 + 0 to 36 + 6 weeks' gestation7, 15.

be reduced by elective Cesarean section or early induction of labor to reduce the inevitable increase in fetal size with advancing gestational age12, 13, 14. As in cases of a SGA neonate, the best prediction of a LGA neonate is achieved by universal sonographic fetal biometry at 35 + 0 to 36 + 6 weeks' gestation7, 15. The most widely adopted model for estimation of fetal weight is the one published by Hadlock et al. in 198516, which combines ultrasonographic measurements of fetal abdominal circumference (AC), head circumference (HC) and femur length (FL) in the formula: Log10 (weight)  = 1.326 − 0.00326 × AC × FL + 0.0107 × HC + 0.0438  × AC.

idely adopted model for estimation of fetal weight is the one published by Hadlock et al. in 198516, which combines ultrasonographic measurements of fetal abdominal circumference (AC), head circumference (HC) and femur length (FL) in the formula: Log10 (weight)  = 1.326 − 0.00326 × AC × FL + 0.0107 × HC + 0.0438  × AC. A systematic review, which identified 46 studies describing a total of 70 models for EFW using various fetal measurements, found the model of Hadlock et al.16 to be the most accurate in predicting the weight of neonates born within 48 h after the scan17. However, data from implementation of a routine scan in 45 847 singleton pregnancies at 35 + 0 to 36 + 6 weeks' gestation showed that screening by sonographic EFW < 10th percentile predicted only 70% of neonates with birth weight < 10th percentile born within 2 weeks after assessment and about 45% of those born at any stage after assessment18. Similarly, EFW > 90th percentile predicted only 71% of neonates with birth weight > 90th percentile born within 10 days after the scan and 46% of those born at ≥ 37 weeks' gestation15. Possible explanations for the performance of EFW being only modest include, first, that some fetuses with EFW > 10th percentile at the time of the scan may become SGA in the subsequent weeks before birth and some of those with EFW < 90th percentile at the time of the scan may become LGA in the subsequent weeks before birth, and, second, that the measurements of AC, HC and FL used in the formula for EFW are imprecise.

with EFW > 10th percentile at the time of the scan may become SGA in the subsequent weeks before birth and some of those with EFW < 90th percentile at the time of the scan may become LGA in the subsequent weeks before birth, and, second, that the measurements of AC, HC and FL used in the formula for EFW are imprecise. The objectives of this study were, first, to obtain measurement‐error models for biometric measurements of AC, HC and FL, and, second, to examine the impact of biometric measurement error on EFW and its effect on the prediction of SGA and LGA fetuses or neonates. METHODS We describe the methodology for examining the effect on EFW of errors in ultrasound measurements of AC, HC and FL taken at 36 + 0 weeks' gestation. At this gestational age, the 10th and 90th percentiles of the EFW distribution are 2453 g and 3086 g, respectively19. There are many combinations of measurements of AC, HC and FL that will provide these estimates. In order to obtain typical combinations, we found median biometry measurements contributing to EFW at earlier and later gestational ages20; at 34 + 3 weeks' gestation, the median values of AC (306.5 mm), HC (316.7 mm) and FL (65.8 mm) produced an EFW of 2453 g, and at 37 + 5 weeks, the median values of AC (333.3 mm), HC (330.3 mm) and FL (70.9 mm) produced an EFW of 3086 g. These combinations of measurements were used as references against which we could assess the effect of biometric measurement errors.

AC (306.5 mm), HC (316.7 mm) and FL (65.8 mm) produced an EFW of 2453 g, and at 37 + 5 weeks, the median values of AC (333.3 mm), HC (330.3 mm) and FL (70.9 mm) produced an EFW of 3086 g. These combinations of measurements were used as references against which we could assess the effect of biometric measurement errors. A multicenter study reported on measurements of fetal AC, HC and FL for 20 313 ultrasound images obtained prospectively from 4321 fetuses at 14–41 weeks' gestation; fetal AC, HC and FL were measured in a blinded fashion in triplicate on separately generated images21. We used data from this study21, which gives 95% limits of agreement for interobserver biometric measurements, to produce estimates of the standard deviations (SD) of individual measurement errors from the true dimension, as described in Appendix S1. Assuming a Gaussian distribution, we chose to examine the effects of errors of ± 0.67 and ± 1.64 SD; under the assumption of a Gaussian distribution, errors with magnitude greater than 0.67 SD occur in 50% of measurements and those with magnitude greater than 1.64 occur in 10% of measurements. Results are presented as EFW and EFW percentiles18 obtained by addition of the errors to the true measurements of AC, HC and FL outlined above.

ion of a Gaussian distribution, errors with magnitude greater than 0.67 SD occur in 50% of measurements and those with magnitude greater than 1.64 occur in 10% of measurements. Results are presented as EFW and EFW percentiles18 obtained by addition of the errors to the true measurements of AC, HC and FL outlined above. To examine the effect of measurement error on the performance of prediction of SGA and LGA, we used a Monte‐Carlo simulation approach. The aim was to explore the effect of measurement error on the classification of fetuses as having EFW < 10th or > 90th percentile. For true percentiles between 0% and 100%, we obtained the proportion of observed EFWs that were < 10th percentile and the proportion of observed EFWs > 90th percentile. When screening for SGA, in a situation with no measurement error, this should be 100% if the true percentile is < 10% and 0% if the true percentile is ≥ 10%. If the true EFW is substantially lower than the 10th percentile and the measurement error results in an EFW ≥ 10th percentile, then the effect of measurement error would be to miss cases of SGA. Conversely, if the true EFW is substantially higher than the 10th percentile and the measurement error results in an EFW < 10th percentile, the effect of measurement error would lead to false positives. Similarly, when screening for LGA, in a situation with no measurement error, this should be 100% if the true percentile is > 90% and 0% if the true percentile is ≤ 90%. If the true EFW is substantially higher than the 90th percentile and the measurement error results in an EFW ≤ 90th percentile, then the effect of measurement error would be to miss cases of LGA. Conversely, if the true EFW is substantially lower than the 90th percentile and the measurement error results in an EFW > 90th percentile, the effect of measurement error would lead to false positives. Taking this one step further, we examined how the specification of percentile cut‐off affects the performance of screening for EFW < 10th and > 90th percentiles.

y lower than the 90th percentile and the measurement error results in an EFW > 90th percentile, the effect of measurement error would lead to false positives. Taking this one step further, we examined how the specification of percentile cut‐off affects the performance of screening for EFW < 10th and > 90th percentiles. For each of the true percentiles, we obtained the gestational age for which the median values of AC, HC and FL20 gave the EFW corresponding to the true percentile as described above for the 10th and 90th percentiles. We then added random errors from an independent Gaussian distribution with SDs obtained from Cavallaro et al.21 to create a sample of 100 000 observed EFWs and computed the proportion that were < 10th and > 90th percentiles at 36 + 0 weeks. To explore the effect of improving measurement precision, we present results using the SDs obtained from the study of Cavallaro et al.21 and for these SDs reduced to 50% of their original value. We also explored screening performance for SGA and LGA using various percentile cut‐offs. The statistical software package R was used for data analysis22. RESULTS The estimated SDs of errors in AC, HC and FL obtained from Cavallaro et al.21, using the method described in Appendix S1, were 6.93 mm for AC, 5.15 mm for HC and 1.38 mm for FL. The corresponding Gaussian distributions of these errors in measurements are shown in Figure 1; FL has the smallest spread and AC has the largest spread. Figure 1 Error distribution of ultrasound measurements of fetal femur length (), head circumference () and abdominal circumference ().

RESULTS The estimated SDs of errors in AC, HC and FL obtained from Cavallaro et al.21, using the method described in Appendix S1, were 6.93 mm for AC, 5.15 mm for HC and 1.38 mm for FL. The corresponding Gaussian distributions of these errors in measurements are shown in Figure 1; FL has the smallest spread and AC has the largest spread. Figure 1 Error distribution of ultrasound measurements of fetal femur length (), head circumference () and abdominal circumference (). UOG-21909-FIG-0001-bTable 1 shows the effect of different combinations of errors in biometry measures on EFW and EFW percentile for true EFWs on the 10th and 90th percentiles. If at 36 + 0 weeks' gestation there were no errors in the measurements of 306.5 mm for AC, 316.7 mm for HC and 65.8 mm for FL, EFW would be 2453 g, which corresponds to the 10th percentile. An error resulting in underestimation in the measurement of AC by 11.4 mm (−1.64 SD), with no error in HC or FL, would result in EFW on the 2.6 percentile (2314 g). In contrast, overestimation of AC by 11.4 mm (+ 1.64 SD), would result in EFW of 2601 g, which corresponds to the 26.5 percentile. Overestimation of AC, HC and FL by 1.64 SD would result in EFW of 2732 g, which corresponds to the 46.9 percentile; overestimation by 0.67 SD would shift EFW to the 21.6 percentile. Conversely, if all measurements are subject to underestimation errors of 1.64 SDs, then EFW would shift from the 10th to the 0.6 percentile, and, with underestimation by 0.67 SD, EFW would be shifted to the 3.7 percentile. Similarly, if at 36 + 0 weeks' gestation there were no errors in the measurements of 333.3 mm for AC, 330.3 mm for HC and 70.9 mm for FL, EFW would be 3086 g, which corresponds to the 90th percentile. An error resulting in underestimation in the measurement of AC by 11.4 mm (−1.64 SD), with no error in HC or FL, would result in EFW on the 75.1 percentile (2923 g). In contrast, overestimation of AC by 11.4 mm (+ 1.64 SD) would result in EFW of 3257 g, which corresponds to the 97.0 percentile. Overestimation of AC, HC and FL by 1.64 SD would result in EFW of 3406 g, which corresponds to the 99.1 percentile; overestimation by 0.67 SD would shift EFW to the 95.9 percentile. Conversely, if all measurements are subject to underestimation by 1.64 SD, then EFW would shift from the 90th to the 55.3 percentile, and, with underestimation by 0.67 SD, the EFW would be shifted to the 79.3 percentile.

rresponds to the 99.1 percentile; overestimation by 0.67 SD would shift EFW to the 95.9 percentile. Conversely, if all measurements are subject to underestimation by 1.64 SD, then EFW would shift from the 90th to the 55.3 percentile, and, with underestimation by 0.67 SD, the EFW would be shifted to the 79.3 percentile. Table 1 Effect of different combinations of error in fetal biometry measurements on estimated fetal weight (EFW) and EFW percentile, for true EFW of 10th and 90th percentiles Error in multiples of SD (mm) Small‐for‐gestational age Large‐for‐gestational age AC HC FL EFW (g) EFW percentile EFW (g) EFW percentile 0.0 0.0 0.0 2453 10.0 3086 90.0 −1.64 (−11.4) 0.0 0.0 2314 2.6 2923 75.1 −0.67 (−4.6) 0.0 0.0 2395 6.1 3018 84.9 0.67 (4.6) 0.0 0.0 2513 15.5 3155 93.7 1.64 (11.4) 0.0 0.0 2601 26.5 3257 97.0 0.0 −1.64 (−8.5) 0.0 2403 6.5 3022 85.3 0.0 −0.67 (−3.5) 0.0 2432 8.4 3059 88.2 0.0 0.67 (3.5) 0.0 2474 11.8 3112 91.6 0.0 1.64 (8.5) 0.0 2505 14.7 3150 93.5 0.0 0.0 −1.64 (−2.3) 2380 5.3 3007 84.0 0.0 0.0 −0.67 (−0.9) 2423 7.8 3053 87.8 0.0 0.0 0.67 (0.9) 2484 12.7 3118 91.9 0.0 0.0 1.64 (2.3) 2529 17.3 3166 94.2 −1.64 (−11.4) −1.64 (−8.5) −1.64 (−2.3) 2194 0.6 2784 55.3 −0.67 (−4.6) −0.67 (−3.5) −0.67 (−0.9) 2345 3.7 2960 79.3 0.67 (4.6) 0.67 (3.5) 0.67 (0.9) 2565 21.6 3214 95.9 1.64 (11.4) 1.64 (8.5) 1.64 (2.3) 2732 46.9 3406 99.1 AC, abdominal circumference; FL, femur length; HC, head circumference.

(2.3) 2529 17.3 3166 94.2 −1.64 (−11.4) −1.64 (−8.5) −1.64 (−2.3) 2194 0.6 2784 55.3 −0.67 (−4.6) −0.67 (−3.5) −0.67 (−0.9) 2345 3.7 2960 79.3 0.67 (4.6) 0.67 (3.5) 0.67 (0.9) 2565 21.6 3214 95.9 1.64 (11.4) 1.64 (8.5) 1.64 (2.3) 2732 46.9 3406 99.1 AC, abdominal circumference; FL, femur length; HC, head circumference. The effect of errors in individual biometry measurements, in mm and SD units, on EFW at 36 + 0 weeks' gestation when true EFW is on the 10th and 90th percentiles (2453 g and 3086 g, respectively), with corresponding biometry measurements as outlined above, can be seen in Figure 2. In terms of SD units, which allow for a fair comparison of the three measures, errors in AC have the largest impact on EFW percentile and errors in HC have the smallest impact on EFW percentile. Figure 2 Effect of error in individual biometry measurements in mm (a) and SD units (b) on estimated fetal weight (EFW) percentile at 36 + 0 weeks' gestation, if true EFW is on 90th percentile (upper lines) or 10th percentile (lower lines), for femur length (), head circumference () and abdominal circumference (). Zero error in remaining two components is assumed for each curve.

in mm (a) and SD units (b) on estimated fetal weight (EFW) percentile at 36 + 0 weeks' gestation, if true EFW is on 90th percentile (upper lines) or 10th percentile (lower lines), for femur length (), head circumference () and abdominal circumference (). Zero error in remaining two components is assumed for each curve. UOG-21909-FIG-0002-bTable 2 shows the performance of screening for SGA and LGA neonates for various percentile cut‐offs, when EFW is obtained using AC, HC and FL subject to random Gaussian errors with means of 0 mm and SD according to Cavallaro et al.21. Assuming errors according to such distributions, when the 10th and 90th percentiles are used to screen for SGA and LGA fetuses, respectively, the detection rates would be 78.0% at false‐positive rates of 4.7%. If the cut‐offs were relaxed to the 30th and 70th percentiles, the detection rates would increase to 98.2%, but at false‐positive rates of 24.2%. Assuming half of the spread in the error distribution, when the 10th and 90th percentiles are used to screen for SGA and LGA fetuses, respectively, the detection rates would be 86.6% at false‐positive rates of 2.3% and, if the cut‐offs were relaxed to the 15th and 85th percentiles, respectively, the detection rates would increase to 97.0% and the false‐positive rates would increase to 6.3%.

90th percentiles are used to screen for SGA and LGA fetuses, respectively, the detection rates would be 86.6% at false‐positive rates of 2.3% and, if the cut‐offs were relaxed to the 15th and 85th percentiles, respectively, the detection rates would increase to 97.0% and the false‐positive rates would increase to 6.3%. Table 2 Performance of screening for small‐ (SGA) and large‐ (LGA) for‐gestational‐age neonates for various estimated fetal weight (EFW) percentile cut‐offs, when EFW is obtained by abdominal circumference, head circumference and femur length, subject to random Gaussian errors with means of 0 mm and estimated SDs of errors according to Cavallaro et al. 21 , and after reduction of these errors by 50%

‐age neonates for various estimated fetal weight (EFW) percentile cut‐offs, when EFW is obtained by abdominal circumference, head circumference and femur length, subject to random Gaussian errors with means of 0 mm and estimated SDs of errors according to Cavallaro et al. 21 , and after reduction of these errors by 50% EFW percentile cut‐off for: DR FPR PPV 100 – NPV n based on population of 100 000 SGA LGA (%) (%) (%) (%) TP FP FN TN SDs as per Cavallaro et al. 10th 90th 78.0 4.7 64.7 2.5 7800 4250 2200 85 750 15th 85th 87.9 9.6 50.5 1.5 8790 8611 1210 81 389 20th 80th 94.4 14.6 41.9 0.7 9440 13 101 560 76 899 25th 75th 97.4 19.7 35.5 0.4 9740 17 692 260 72 308 30th 70th 98.2 24.2 31.1 0.3 9820 21 772 180 68 228 35th 65th 99.2 29.2 27.4 0.1 9920 26 303 80 63 697 40th 60th 99.4 34.3 24.4 0.1 9940 30 863 60 59 137 45th 55th 99.7 39.6 21.8 0.0 9970 35 664 30 54 336 50th 50th 100.0 43.6 20.3 0.0 10 000 39 274 0 50 726 SDs reduced by 50% 10th 90th 86.6 2.3 80.9 1.5 8660 2040 1340 87 960 15th 85th 97.0 6.3 62.9 0.4 9700 5711 300 84 289 20th 80th 99.7 11.6 48.8 0.0 9970 10 461 30 79 539 25th 75th 100.0 16.7 40.0 0.0 10 000 15 012 0 74 988 30th 70th 100.0 22.4 33.1 0.0 10 000 20 192 0 69 808 35th 65th 100.0 28.0 28.4 0.0 10 000 25 193 0 64 807 40th 60th 100.0 33.5 24.9 0.0 10 000 30 123 0 59 877 45th 55th 100.0 38.5 22.4 0.0 10 000 34 654 0 55 346 50th 50th 100.0 44.1 20.1 0.0 10 000 39 664 0 50 336 DR, detection rate; FN, false negative; FP, false positive; FPR, false‐positive rate; NPV, negative predictive value; PPV, positive predictive value; TN, true negative; TP, true positive.

0.0 10 000 30 123 0 59 877 45th 55th 100.0 38.5 22.4 0.0 10 000 34 654 0 55 346 50th 50th 100.0 44.1 20.1 0.0 10 000 39 664 0 50 336 DR, detection rate; FN, false negative; FP, false positive; FPR, false‐positive rate; NPV, negative predictive value; PPV, positive predictive value; TN, true negative; TP, true positive. Figure 3 shows the proportion of fetuses with EFW < 10th and > 90th percentiles for true percentiles from 0–100. On the basis of estimated SDs of errors obtained from Cavallaro et al.21, of those with EFW truly on the 10th percentile, only 50% would be classified as SGA due to measurement error. Of those with true EFW on the 20th percentile, approximately 16% would be classified as < 10th percentile due to measurement error. Of those with true EFW on the 5th percentile, approximately 20% would be classified as ≥ 10th percentile due to measurement error. The performance of EFW < 10th percentile improves with decreasing error variation. Similarly, due to the symmetry of the distribution of percentiles, of those with EFW truly on the 90th percentile, only 50% would be classified as LGA due to measurement error. Of those with true EFW on the 80th percentile, approximately 16% would be classified as > 90th percentile. Of those with true EFW on the 95th percentile, approximately 20% would be classified as ≤ 90th percentile due to measurement error.

he 90th percentile, only 50% would be classified as LGA due to measurement error. Of those with true EFW on the 80th percentile, approximately 16% would be classified as > 90th percentile. Of those with true EFW on the 95th percentile, approximately 20% would be classified as ≤ 90th percentile due to measurement error. Figure 3 Proportion of fetuses with estimated fetal weight (EFW) < 10th percentile (a) and > 90th percentile (b) for true EFW percentiles from 0–100, with estimated SDs of errors (6.93 mm for abdominal circumference, 5.15 mm for head circumference and 1.38 mm for femur length) obtained from Cavallaro et al.21 () and with SDs of errors reduced by 50% (). UOG-21909-FIG-0003-bDISCUSSION Main findings The findings of this study demonstrate that, even with a comprehensive package of ultrasound quality control21 and use of the most widely accepted and accurate model for EFW16, 17, errors in measurements of fetal AC, HC and FL have a large impact on EFW and therefore EFW percentile. Even relatively small errors in a single component parameter can alter potential clinical decisions, with either an appropriate‐for‐gestational‐age (AGA) fetus being classified as SGA or LGA, or a SGA or LGA fetus being classified as AGA. Comparison with previous studies There is extensive literature on the use of measurement‐error models such as the one we applied in this study23. We are not aware of other work in which error SDs have been extracted from limits of agreement. However, this is an application of standard distribution theory.

UOG-21909-FIG-0003-bDISCUSSION Main findings The findings of this study demonstrate that, even with a comprehensive package of ultrasound quality control21 and use of the most widely accepted and accurate model for EFW16, 17, errors in measurements of fetal AC, HC and FL have a large impact on EFW and therefore EFW percentile. Even relatively small errors in a single component parameter can alter potential clinical decisions, with either an appropriate‐for‐gestational‐age (AGA) fetus being classified as SGA or LGA, or a SGA or LGA fetus being classified as AGA. Comparison with previous studies There is extensive literature on the use of measurement‐error models such as the one we applied in this study23. We are not aware of other work in which error SDs have been extracted from limits of agreement. However, this is an application of standard distribution theory. Clinical implications There is now good evidence that, first, about 85% of SGA neonates are born at term6, second, the best way to identify such SGA and LGA fetuses is by routine sonography at 35 + 0 to 36 + 6 weeks' gestation6, 7, 8, 9, 15, and, third, the most accurate model for assessment of EFW is that reported by Hadlock et al., which combines ultrasonographic measurements of fetal AC, HC and FL16, 17. However, as demonstrated in this study, measurement error in fetal biometry can cause substantial error in EFW, resulting in misclassification of both SGA and LGA neonates.

accurate model for assessment of EFW is that reported by Hadlock et al., which combines ultrasonographic measurements of fetal AC, HC and FL16, 17. However, as demonstrated in this study, measurement error in fetal biometry can cause substantial error in EFW, resulting in misclassification of both SGA and LGA neonates. There are three potential approaches for improving the performance of prenatal prediction of SGA and LGA neonates. First, improving the models for assessment of EFW; but, despite many efforts in the last 50 years and the publication of more than 70 models, the one reported by Hadlock et al. in 1985 remains the most widely accepted and accurate one16, 17. Attempts at improving the prediction of birth weight by the addition of maternal characteristics to fetal biometry have not been found to be successful24, 25. Similarly, there is some contradictory evidence as to whether the precision of EFW can be improved by three‐dimensional ultrasound volumetry26, 27, 28. Second, development of a standardized fetal biometric ultrasound measurement protocol, involving training, assessment and certification of sonographers and both qualitative and quantitative quality‐control monitoring, can minimize systematic error and ensure high reproducibility21. As demonstrated in this study, it would be necessary to improve this process further to reduce errors in measurements and this could potentially be achieved by sonographers repeating measurements when EFW is near the cut‐off of interest, such as the 10th or 90th percentile. The third approach for potential improvement of the performance of prenatal prediction of adverse perinatal outcome in pregnancies undergoing routine ultrasound examination at 35 + 0 to 36 + 6 weeks' gestation is to accept the limitations of sonographic EFW at the cut‐offs of the 10th and 90th percentiles, respectively, and base clinical management, including serial scans, on an EFW cut‐off of the 40th percentile together with findings of fetal Doppler indices for SGA fetuses18 and the 70th percentile for LGA fetuses15.

tion is to accept the limitations of sonographic EFW at the cut‐offs of the 10th and 90th percentiles, respectively, and base clinical management, including serial scans, on an EFW cut‐off of the 40th percentile together with findings of fetal Doppler indices for SGA fetuses18 and the 70th percentile for LGA fetuses15. Strengths and limitations Use of a Monte‐Carlo simulation approach allows examination of the effect of measurement error on EFW and clinical interpretation in the hypothetical situation in which the true biometric measurements are known. We can also explore the effect of different levels of variability on EFW, enabling us to set acceptable limits on the level of error variability.

ach allows examination of the effect of measurement error on EFW and clinical interpretation in the hypothetical situation in which the true biometric measurements are known. We can also explore the effect of different levels of variability on EFW, enabling us to set acceptable limits on the level of error variability. Limitations are the assumptions of uncorrelated Gaussian distributed errors with constant SDs, centered on zero. In practice, the correlations are likely to be positive which will mean that the errors tend to be in the same direction, increasing their effect on EFW. The assumption that the errors are uncorrelated could therefore be considered as conservative. Another limitation is that, although there are many combinations of biometry that will result in an EFW on the 10th and 90th percentiles, we used median levels at an earlier gestational age for the former and those at a later gestational age for the latter. Consequently, our results apply to the situation in which the fetus has biometry consistent with an earlier gestational age for EFW on the 10th percentile, and a later gestational age for EFW on the 90th percentile. Standard deviations were obtained from a study with a comprehensive package of ultrasound quality control21. However, interobserver comparisons relate to caliper placement by different individuals using the same image; this ignores variations between images, leading to underestimation of error variation. In other settings, measurement‐error SDs may differ from those assumed here due to differences in populations, equipment, quality‐control procedures and other factors.

caliper placement by different individuals using the same image; this ignores variations between images, leading to underestimation of error variation. In other settings, measurement‐error SDs may differ from those assumed here due to differences in populations, equipment, quality‐control procedures and other factors. Conclusions Measurement error in fetal biometry causes substantial error in EFW, resulting in misclassification of SGA and LGA fetuses. This explains, to a certain extent, the limited performance of sonographic EFW in screening for SGA and LGA neonates. For reliable assessment of SGA and LGA by EFW, the biometric measurement‐error SDs obtained from Cavallaro et al.21 should be reduced by at least 50%. The extent to which improvement can be achieved through effective quality assurance remains to be seen but, as a first step, it is important for practitioners to understand how biometric measurement error impacts on the prediction of SGA and LGA fetuses. Supporting information Appendix S1 Calculation of error standard deviation Click here for additional data file. ACKNOWLEDGMENT This study was supported by a grant from The Fetal Medicine Foundation (Charity No: 1037116).