Fetal weight estimation, evaluation of different methods

Fetal weight estimation, evaluation of different methods | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Fetal weight estimation, evaluation of different methods Laura Almenar Agustí, Antoni Llueca Abella, Paula Carrasco Espí, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6395306/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Introduction : The accuracy of ultrasound estimation of fetal weight is influenced by the imprecision of ultrasound methods. The aim of this study is to compare the accuracy of fetal weight estimation between conventional (2D) models and those including 2D and 3D subcutaneous tissue measurements. Methods : Prospective study of 199 low-risk pregnant women at the Hospital Universitario La Plana, Vila-Real, Spain. Data acquisition included the 2D and 3D parameters described by Lee, Hadlock and Scioscia. Results : All ultrasound formulas correlated positively with actual birth weight calculated by Sperman's method. Hadlock's model has the highest correlation with a different percentage 3000 g, Hadlock’s method was more accurate, 50% of the fetuses had less than 5% difference calculated as mean percentage difference, in newborns < 3000 g Lee’s method obtained more accurate results, 68.8% of the estimated weigths were within 5% of birth weight. Overall, systematic and random errors were lower for the Hadlock models than for the Lee and Scioscia models. Conclusions : The inclusion of fractional thigh volume in the ultrasound estimation of fetal weight improves the estimation of fetuses weighing less than 3000g. In our study Hadlock's method provided more accurate estimates of fetal weight in fetuses with normal range growth. Health sciences/Health care/Medical imaging Health sciences/Health care Ultrasound Soft tissue Fractional thigh volume Three-dimensional models Fetal growth Figures Figure 1 Introduction The estimated fetal weight is considered a key parameter for monitoring fetal growth and allows for early diagnosis of normal and abnormal patterns such as fetal growth restriction (FGR) or macrosomia that are strongly associated with an increased risk of adverse outcomes at birth 1 – 3 . Growth-restricted fetuses have increased risk for admission to neonatal intensive care units, low Apgar scores, neurological injury, stillbirth or early neonatal death 4 , 5 . Fetal macrosomia is associated with an increased risk of shoulder dystocia, or asphyxia, as well as maternal complications, such as prolonged labor, instrumental delivery, cesarean section, postpartum hemorrhage or uterine rupture 6 , 7 . A reliable fetal weight estimation of birthweight is important to reduce adverse outcomes and to optimize clinical decisions during gestational follow-up, the timing and delivery route and therapeutic interventions. In addition, an accurate measurement of the gestational age of fetal growth may facilitate an adequate diagnosis of fetuses with growth disorders, avoiding iatrogenic medicalization of the pregnancy 8 , 9 . However, the accuracy of prenatal weight estimation remains a challenge especially when fetal growth is suboptimal 10 – 14 At present, ultrasonography is the best method for dating gestation, monitoring fetal growth during pregnancy and diagnosing fetal growth disorders 1 , 15 – 17 . There are numerous regression formulas for the estimation of fetal weight via two-dimensional (2D) ultrasound, consisting of different combinations of fetal biometric measurements including head circumference (HC), biparietal diameter (BPD), femur length (FL) and abdominal circumference (AC). 18 – 20 . In addition, human fetuses have the greatest proportion of body fat among mammalian species and an enormous capacity for altering this body compartment as a result of intrauterine growth 12 . Accurate evaluation of soft tissue masses could provide additional insight into physiological adaptations to intrauterine growth disturbances. Postnatal investigations suggest that soft tissue assessment should provide additional insight into generalized fetal nutritional status 2 , 6 , 9 , 19 , 21 , 22 Despite their clinical value, accurate estimation of fetal weight and prediction of macrosomia or growth retardation are challenging, with significant margins of error for both clinical estimates and routine two-dimensional (2D) ultrasound biometry, most of which are present at the extremes of fetal weight. 8 If a measurement of limb soft tissue volume is added to 2-dimensional (2D) fetal biometry, it could substantially improve the accuracy of these estimates. Conventional fetal weight prediction models typically include 2D ultrasound measurements of the head circumference, abdominal circumference, and femur diaphysis length 23 , regardless of soft tissue development such as what could be provided with fractional limb volume (FLV). 8 , 24 – 27 Materials and methods Study design and subjects We conducted a prospective study of fetal biometry via 2D and 3D ultrasound at the Gynecology and Obstetrics Unit of La Plana University Hospital in Castellón, a Mediterranean region in eastern Spain. The study population included pregnant caucasian women (n = 236) who were invited to participate before the 40th week of scaning between January 2019 and October 2022. The inclusion criteria were: women 18 years or older with uncomplicated singleton pregnancies monitored at the La Plana Hospital, sonographic evidence of normal amniotic fluid, and fetuses with no structural or chromosomal alterations, who presented as cephalic and delivered between 37 + 0 and 41 + 6 weeks of gestation. We excluded women with psychiatric or cognitive pathology that prevented understanding and consent to the study, women taking drugs or other teratogenic products during gestation or who had induced or spontaneous abortions or fetal deaths. Of the 236 women recruited, 208 attended the last ultrasound scan and were followed up until delivery. Fetuses that gave birth more than 10 days after the ultrasound scan (n = 5) and fetuses that lacked documentation of all biometric measurements in the last scan and birth weight (n = 4) were excluded, the final population included in this study included 199 participants. Information about maternal age at conception, previous gestation, maternal prepregnancy body mass index (BMI [kg/m 2 ]), maternal weight gain during pregnancy, maternal active smoking during pregnancy and the presence of gestational diabetes was collected via a questionnaire during the scan visit. The study was approved by the Ethics and Clinical Research Committee of the Hospital de La Plana in October 2018. Written informed consent for participation and data usage was obtained from all participants and from the parents or legal guardians of each fetus/newborn included in the study. All experiments were performed according to the relevant guidelines. Ultrasonic data collection Women had 2D and 3D ultrasonography scans for estimation of fetal weight between 37 + 0 and 41 + 6 weeks of gestation within 10 days of delivery. Gestational age at the time of sonographic evaluation was calculated by the date of the onset of the last menstrual period confirmed and modified by first trimester ultrasound data if the discrepancy between them exceeded 5 days 1 . The estimated fetal weight was calculated according to different regression models published in the literature and presented in Table 1 to compare their accuracy in this study. These formulas include standard 2D sonographic parameters, i.e (BPD, HC, AC and FL) (18), the 3D automated fractional thigh volume (TVol) described by Lee 26 and the 2D soft tissue of the femur (STT) described by Scioscia 28 in Table 1 . Table 1 Formulas used for sonographic fetal weight estimation Hadlock II (1985) 23 - Required ultrasound parameters: BPD, HC, AC, FL - log 10 EFW = 1.3596 + 0.0064(CC) + 0.0424(CA) + 0.174(FL) + 0.00061(DBP)(CA) -0.00386(CA)(FL) Lee (2004) 18 - Required ultrasound parameters: BPD, CA, TVol - Ln BW = -0.8297 + 4.0344 (ln BPD) - 0.7820 (ln BPD)2 + 0.7853 (ln AC) + 0.0528 (ln TVol) 2 Scioscia (2008) 32 - Required ultrasound parameters: STT, FL - EFW = -1687.47 + (54.1 × FL) + (76.68 × STT) Note: EFW: Estimated foetal weight, BPD: Biparietal diameter, HC: Head circumference, AC: Abdominal Measurement of BPD and HC was obtained by means of a cephalic transverse plane in which the midline was observed, allowing visualization of the interhemispheric fissure and the cavum of the septum pellicidum 23 . The assessment of the CA was based on an abdominal cross section including the umbilical vein and the origin of the gastric chamber 23 . At the level of the extremities we obtained the FL, STT and TVol in the longitudinal plane of the femur closest to the transducer where the femoral diaphysis was observed 9 , 23 , 26 . Ultrasound scanning was performed with a General Electrics Voluson E6 ultrasound scanner, which has a probe capable of calculating volumes. Each scanning sweep lasted approximately 10–15 minutes and the volume data were stored in removable digital media for later analysis. All fetal measurements were performed by an experienced physician in obstetric ultrasound. Postnatal assessment Perinatological data were collected using the newborn identification register and medical records were reviewed. Birth weights were recorded at delivery and were stratified as follows: 4000g. Additionally weeks of gestation, delivery type (eutocic, instrumental or caesarean), days between the last scan and delivery and newborn sex were documented. Statistical analysis First, a descriptive analysis was conducted for maternal, obstetrical and fetal characteristics in the overall sample of study participants and according to birth-weight classification. Categorical variables are expressed as frequencies (percentages). Continuous variables were expressed as the mean (standard deviation [SD] as dispersion measure) when they followed a normal distribution. Non-normally distributed continuous variables are reported as medians (interquartile range [IR]). Normality of continuous variables was measured by examining density plots and histograms. The correlation between estimated fetal weight and birth weight was estimated using the Spearman correlation coefficient. The linear regression line between each of the prediction methods and the actual birth weight and the goodness of fit with R 2 were also obtained and plotted in scatter diagrams. The performance of the three formulas for estimating fetal weight was assessed by accuracy (systematic error) and precision (random error) as previously proposed 29 . Sistematic error was calculated from the signed mean percent difference with the birth weight using the following formula: estimated weight – BW)/ BW × 100. Random error was calculated as the standard deviation of percent differences. The proportion of neonates whose estimated fetal weight was within 5% and 10% of BW were also calculated. For each regression model the mean square error (MSE) was computed as an unbiased estimator of the population variance 21 P < 0.05 was considered to indicate statical significance. All analyses were performed with R version 4.4.3. Results Demographic and obstetrical characteristics of the study population (n = 199) are presented in Table 2 . The mean (SD) age of pregnant women participating in this study was 33.0 (4.8) years with an average number of previous pregnancies of 2.1 (1.0). The mean BMI was 24.4 (4.6) kg/m 2 and mean weight gain during pregnancy was 12.1 (6.6) kg, with no differences found between pregnant women according to birth weight of the newborn classification. Approximately 15.7% of mothers smoked during pregnancy. Nineteen cases (9.6%) of gestational diabetes have been detected. The majorityof deliveries were eutocic (69.5%), 12.6% instrumented and 17.9% by cesarean section. The percentage of cesarean section deliveries was higher in mothers with newborns weighing more than 4000 g at birth (43.8%). Pregnant women were scanned with a mean (SD) of 5.6 (3.2) days before delivery. About 51,3% of neonates were female and 48.7% male and were delivered at a mean ± SD gestational age of 39.9 ± 0.6 weeks. Female sex was higher among neonates born at less than 3000 g (75%). The overall mean (SD) of birth weight was 3,482 (379) g (range 2660–4785 g). Table 2 Descriptive measures of the study population and according to the birth weight classification Overall n = 199 < 3000 g n = 16 3000–3499 g n = 95 3500–3999 g n = 72 ≥ 4000 g n = 16 p-value a Maternal characteristics Age, mean (SD) years 33.0 (4.8) 32.0 (5.1) 32.8 (4.4) 33.1 (5.4) 35.4 (3.9) 0.173 Previous gestations, mean (SD) 2.1 (1.0) 1.5 (0.9) 2.0 (1.0) 2.2(0.9) 2.3(1.1) 0.050 Pre-pregnancy height, mean (SD) kg 1.64 (0.06) 1.64 (0.07) 1.63 (0.06) 1.65 (0.06) 1.66 (0.05) 0.302 Pre-pregnancy BMI, mean (SD) kg/m 2 24.4 (4.6) 23.6 (4.5) 24.8 (4.6) 24.0 (4.9) 24.7 (3.5) 0.677 Weight gain, mean (SD) (kg) 12.1 (6.6) 10.8 (2.2) 11.5 (8.2) 12.8 (4.5) 15.5 (5.4) 0.300 Smoking in pregnancy (yes), n (%) 31 (15,7%) 3 (18,8%) 18 (18,9%) 10 (13,9%) 0 (0%) 0.282 Gestational diabetes mellitus (yes), n(%) 19 (9.6%) 1 (6.3%) 10 (10.5%) 7 (9.9%) 1 (6.7%) 0.976 Weeks of gestation, mean (SD) 39.9 (0.6) 39.9 (0.4) 39.9 (0.6) 39.9 (0.6) 39.8 (0.8) 0.926 Delivery type 0.119 Eutocic 132 (69.5%) 9 (69.2%) 66 (72.5%) 50 (71.4%) 7 (43.8%) Instrumented 24 (12.6%) 3 (23.1%) 10 (11.0%) 9 (12.9%) 2 (12.5%) Caesarean 34 (17.9%) 1 (7.69%) 15 (16.5%) 11 (15.7%) 7 (43.8%) Days between last scan and delivery, mean (SD) 5.6 (3.2) 4.6 (2.9) 5.3 (3.2) 6.2 (3.3) 6.4 (2.8) 0.117 Newborn’s characteristics Birthweight mean (SD) g 3482 (379) 2880 (103) 3274 (136) 3712 (142) 4291 (251) <0.001 Median (IR) g 3430 (3238;3740) 2900 (2848;2962) 3280 (3190;3400) 3725 (3585;3821) 4172 (4092;4520) < 0.001 Sex (female), n(%) 102 (51.3%) 12 (75.0%) 53 (55.8%) 31 (43.1%) 6 (37.5%) 0.056 Note: n sample size; SD standard deviation; IR interquartile range; BMI body mass index; Continuous variables are presented as mean (SD); categorical values are n (%). a p-value from the chi-square test (categorical variables),ANOVA test when comparing means and Kruskall-Wallis test when comparing medians. Correlation analysis between the estimated weights using the different formulas and birth weight is summarized in Table 3 . A positive correlation was observed in all cases but the estimates using Hadlock and Lee models were higher (r = 0.68 IC95% [0.65;0.75] and (r = 0.67 IC95% [0.59;0.75], respectively) than that observed using the Scioscia model (r = 0.29 IC95% [0.15;0.42]). Figure 1 shows the relationships between the estimated weights and birth weight in a scatter plot including the coefficient of determination R 2 . Linear regression line better fits the actual birth weight for the Hadlock and Lee models than for the Scioscia model. Table 3 Performance summary of formulas that estimate foetal weight (Hadlock et al, Lee et al and Scioscia et al) with respect to birth weight. Overall n = 199 < 3000 g n = 16 3000–3499 g n = 95 3500–3999 g n = 72 ≥ 4000 g n = 16 Correlation with BW (r, 95% CI) a Hadlock et al. 0.68 (0.65;0.75) 0.03(-0.49;0.53) 0.44(0.26;0.60) 0.42(0.20;0.60) 0.40(-0.14;0.75) Lee et al. 0.67 (0.59; 0.75) 0.21(-0.34;0.65) 0.32(0.12;0.50) 0.30(0.06;0.50) 0.20(-0.34;0.64) Scioscia et al. 0.29 (0.15;0.42) -0.13(-0.61;0.43) 0.13(-0.08;0.33) 0.11(-0.14;0.34) -0.38(-0.75;0.18) Systematic error ( random error) b Hadlock et al. 4.3 (8.0) 12.8 (7.2) 7.0 (6.4) 0.5 (7.1) -4.0 (6.4) Lee et al. -9.3 (7.9) -2.3 (5.3) -8.1 (7.8) -11.4 (6.8) -14.4 (9.1) Scioscia et al. 5.5 (12.9) 20.3 (11.9) 10.0 (11.1) -0.3 (9.6) -9.8 (9.1) Absolute mean error, n(% of cases) c Hadlock et al. 10% 55 (27.6%) 12 (75.0%) 29 (30.5%) 11 (15.3%) 3 (18.8%) Lee et al. 10% 92 (46.5%) 1 (6.25%) 37 (38.9%) 41 (57.7%) 13 (81.2%) Scioscia et al. 10% 90 (45.9%) 12 (80.0%) 50 (52.6%) 21 (29.6%) 7 (46.7%) RMSE Hadlock et al. 303.9 416.2 307.2 261.5 327.1 Lee et al. 446.8 166.4 368.7 495.0 740.3 Scioscia et al. 468.4 665.1 482.1 352.8 603.1 Note: BW: birth weight; RMSE: root mean square error a r: Spearman correlation coefficient; CI: confidence intervals b Systematic error was calculated from the signed mean percent difference as estimated weight – BW)/BW × 100; random error was calculated as the standard deviation of percent differences. c Absolute frequency and proportion of neonates with fetal weight predictions within 10% of actual BW The systematic errors, random errors, RMSE and prediction rates within, 5%, 5–10% and over 10% are summarized in Table 2 . In general, systematic and random errors were lower for Hadlock than for Lee and Scioscia models. For the Hadlock model the mean systematic error was 4.3% and the random error was 8.0%. For newborns with weights < 4000 g, the predicted birth weights were slightly overestimated (range, 0.5; 12.8%) and for the 4000 g threshold, the estimated birth weights were slightly underestimated (-4.01%). Random errors were similar for all the subgroups (range, 6.4–7.2%). For the Lee model the mean systematic error was − 9.3% and the random error was 7.9% which was negative for all groups suggesting an underestimation of birth weight. However, systematic error was lower for the neonates in the < 3000 g group than for those in the higher weight groups, with the highest error observed in the group weighing ≥ 4000 g (range, -2.3; -14.4%). Random error was also lower in the group weighing < 3000 g and higher in the group weighing 4000 g or more (range, 5.3; 9.1%). For the Scioscia model the mean systematic error was 5.5% and random error 12.9%. The highest systematic error was observed in the < 3000 g group (20.3%) which improved in the 3500–3999 g group (-0.3%). It was positive in neonates weighing less than 3500 g and negative when the birth weight was higher. Random errors were similar for all subgroups (range, 9.1–11.9%). For neonates with a birth weight < 3000 g, the lowest percentage of systematic and random error was observed for the Lee model (-2.3 [5.3]%) and the highest for the Scioscia model (20.3 [11.9]%). For newborns having a birth weight between 3000 g and 3499 g, estimated birth weight using Hadlock model had slightly smaller systematic and random error (7.0 [6.4]%) than the other two models. For neonates with a birth weight between 3500 g and 3999 g, the systematic error was considerably reduced for the Hadlock and Scioscia models although the random error was similar for all three models (range, 6.8–9.6%). When birth weight exceeded 4000 g, the lowest systematic and random error was observed for Hadlock model (-4.0 [6.4]%) and increased to -9.8 (9.1)% and − 14.4 (9.1)% respectively for Scioscia and Lee models. For the Hadlock model, the proportion of neonates for whom the percentage difference between estimated and observed birthweight was less than 5% and between 5–10%, were higher (42.7% and 29.6%, respectively) than for the Lee and Scioscia models. However, the prediction rate within 5% and between 5–10% of birth weight for the Lee model was higher (68.8% and 25%, respectively) than those for Hadlock and Scioscia models in newborns with less than 3000 g at birth. However, the percentage is lower in newborns over 3000 g at birth, reaching 0% in newborns of 4000 g or more. In newborns over 3000 g, the method described by Hadlock obtained a greater number of cases with less than 5% of difference error between estimated and observed birthweight. In newborns over 3500 g this method showed a difference of less than 5% in more than 50% of the cases. RMSE values for Hadlock model were lower compared to Lee and Scioscia models for all newborns and those with birth weight 3000 g or more. The Lee model provided lowest RMSE value For neonates with a birth weight < 3000 g and highest for neonates with a birth weight between 3500 g and 3999 g and over 4000 g. Discussion Birth weight is a well-established predictor of neonatal and child morbidity and mortality 10 Therefore, obstetricians should be able to obtain the most valid fetal weight estimations for guiding clinical decisions as a routine part of prenatal care. 30 Estimated fetal weight is a composite parameter that can be calculated by different methods. In routine clinical practice it is usually assessed with the formula described by Hadlock 14 , 23 , in wich only skeletal measurements are used (DBP, CC, AC, FL) and does not include soft tissue parameters. Commonly used fetal ultrasound mesurements for prediction of estimation fetal weight can only predict birth weight with modest accuracy, and these measurements are limited for prediction of percentage of neonatal fat, wich accounts 46% of the variance in birth weight. 13 , 26 Several investigators have used 3D ultrasonography to demonstrate a significant correlation between fetal limb volume and birth weight 8 , 25 , 27 Scioscia published a prospective study evaluating the performance of an equeation based on the linear measurement of the soft tissue above the external fetal femur. Their new formula showed a reduced standard desviation, which means a lower internal error in prediction. 9 , 28 Lee conducted a prospective, cross-sectional study of 164 pregnant women with singleton fetuses in the second and third trimesters of pregnancy, were compared the accuracy of fetal weigth estimation using fractional thigh volumen with a Hadlock’s formula. This publication showed that the prediction model that incorporated fractional thigh volume correctly classified a greater proportion of EFW within 5% of BW compared with the modified Hadlock model, (16) Subsequently, Lee et al examined more 300 pregnancies in a prospective multicenter study they concluded that the inclusion of automated FLV measurements with conventional 2-dimensional biometry was generally associated with improved weight predictions, which has also been reported by other authors 21 , 27 Our results show that all three ultrasound formulas obtained a positive correlation with the real birth weight. The most accurate results were obtained with the weight estimation method described by Hadlock (r = 0.68) but very similar to that described by Lee (RHO 0.67). In fetuses under 3000 g the method described by Lee, using soft tissue measured as fractional volume of the thigh, has obtained more accurate estimates. The size of these fetuses is similar to the mean of 34–36 week fetuses, which are those evaluated in previous studies. 2 This finding in fetuses over 3000 g is different from those of previous studies, in which the estimated values obtained using soft tissue parameters were superior 18 . A practical challenge for using FLV is the time it takes to manually trace limb circumferences via trackball in routine clinical practice. The sonographic data were saved and analyzed on delay with the 4Dview software ang through the TUI application. The central axis of the thigh is marked (following the femoral diaphysis) and the central 50% is automatically subdivided into 5 equidistant slices. The areas of each of these slices were manually plotted to obtain the fractional volume 18 . Although possible to perform automated FLV measurements to provide a faster and more practical means for adding a soft tissue component to the fetal weight estimation process. Reproducible computer assisted FLV measurements are approximately 5 times faster than manual methods, which otherwise require 2.5 to 3.0 minutes to be completed. 19 All our ultrasound scans had been performed approximatelly 40th weeks of gestation, later than most of the studies evaluating different methods of fetal weight estimation examine fetuses of lower gestacional age. 2 , 21 , 25 , 31 Only in fetuses under 3000 g the method described by Lee obtained more accurate estimates. These findings agreed with those previously presented, as the fetuses are the smallest in size and it’s possible to obtain the echographic planes more easily and we could better delimit the fetal parts. Is possible that the older gestational age, under normal conditions, and the larger and heavier the fetus is, the more difficult is to obtain the correct ultrasound planes without putting excesive pressure on the fetal parts. In the last weeks of gestation differentiatig the fetal parts renders it difficult to obtain accurate values. 27 Theses circumstances may have interferred the discrepancies with some studies published, since we haven’t obtained a benefit in the accuracy of fetal weight estimation by adding soft tissue parameters. The main strengths of our work include prospective data collection from a low-risk population, we have analyzed fetal weights with little margin to delivery, and all ultrasound estimates of late gestation were taken by the same obstetrician at the same ultrasound examination. The main limitation has been the relative paucity of research participants with fetuses with abnormal growth, due to the selection of our low-risk population many of them were discarded before recruitment, as many of them had already been indicated for termination. Therefore, the percentage of fetuses with growth below the 3rd percentile are the patients who have been overestimated in previous biometries, having a small sample of this population where it is trully more important in clinical practice to be more accurate. In conclusion, our results are in line with current literature and recommendations. The most accurate estimate of fetal weight was obtained with the formula described by Hadlock for the total population. 20 The usefulness of volume or fetal soft tissue parameters has been observed in the subgroup of smaller fetuses, where it is necessary to be more precise at the clinical level, thus this option is promising because it is more accurate in our routine clinical practice and generates less iatrogenesis. Abbreviations - FGR - Fetal growthrestriction - BW - Birth weight - EFW - Estimated fetal weight - HC - Head circumference - BDP - Biparietal diameter - FL - Femur length - AC - Abdominal circumference - FLV - Fractional limb volume - STT - Soft tissue in the medial thigh area. Declarations Funding: We haven’t obtained external financing Conflic of interest : None declared. Data Availability Statement : The data is provided in the manuscript or in the supplementary information files, For more information on the data, please contact the corresponding author or first author. If you need the data to be published in any entity, please let us know. Author Contribution E.M. Has designed the study to be carried out and has selected the target population on which to perform it. Has reviewed and contributed in the sampling of patients and in the revision of the manuscript.P.C. Has directed and carried out the main part of the statistical study, has written the statistical method part of manuscript, has elaborated the tables and has reviewed the entire manuscript.L.A. Has carried out the literature review and designed the study, collected most of the data for the study (sonographic data, all the data on the pregnant women an the all perinatal data), selected the patients who met the informed criteria and explained the informed consent. L.A. Has prepared de database, analyzed the results and has written a large part of the manuscript. A. L. Has reviewed the study design and made modifications, reviewed the data collected and revised the manuscript. Acknowledgement: We would like to thank the La Plana University Hospital for the possibility of carrying out this study, and the University Jaume I (UJI) for their collaboration. References Arenas Ramírez, J. & GUÍA DE LA EXPLORACIÓN ECOGRÁFICA DE III TRIMESTRE Guía de Asistencia. 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Ultrasound for fetal assessment in early pregnancy. Cochrane Database of Systematic Reviews vol. 2015 Preprint at (2015). https://doi.org/10.1002/14651858.CD007058.pub3 Mul, T., Mongelli, M. & Gardosi, J. A comparative analysis of second-trimester ultrasound dating formulae in pregnancies conceived with artificial reproductive techniques. Ultrasound in Obstetrics and Gynecology vol. 8 397–402 Preprint at (1996). https://doi.org/10.1046/j.1469-0705.1997.08060397.x Mongelli, M., Chew, S., Yuxin, N. G. & Biswas, A. Third-trimester ultrasound dating algorithms derived from pregnancies conceived with artificial reproductive techniques. Ultrasound Obstet. Gynecol. 26 , 129–131 (2005). Lee, W. et al. Individualized growth assessment of fetal soft tissue using fractional thigh volume. Ultrasound Obstet. Gynecol. 24 , 766–774 (2004). Lee, W. et al. Fetal Weight Estimation Using Automated Fractional Limb Volume With 2-Dimensional Size Parameters: A Multicenter Study. J. Ultrasound Med. 39 , 1317–1324 (2020). Hammami, A., Mazer Zumaeta, A., Syngelaki, A., Akolekar, R. & Nicolaides, K. H. Ultrasonographic estimation of fetal weight: development of new model and assessment of performance of previous models. Ultrasound Obstet. Gynecol. 52 , 35–43 (2018). Lee, W. et al. New fetal weight estimation models using fractional limb volume. Ultrasound Obstet. Gynecol. 34 , 556–565 (2009). Moore, G. S. et al. American Institute of Ultrasound in Medicine,. Can fetal limb soft tissue measurements in the third trimester predict neonatal adiposity? in Journal of Ultrasound in Medicine vol. 35 1915–1924 (2016). Hadlock, F. P., Harrist, R. B., Sharman, R. S., Deter, R. L. & Park, S. K. Volume 151 Number 3. (1985). Lee, W. et al. Fractional limb volume - A soft tissue parameter of fetal body composition: Validation, technical considerations and normal ranges during pregnancy. Ultrasound Obstet. Gynecol. 33 , 427–440 (2009). Lee, W., Deter, R., Sangi-Haghpeykar, H., Yeo, L. & Romero, R. Prospective validation of fetal weight estimation using fractional limb volume. Ultrasound Obstet. Gynecol. 41 , 198–203 (2013). Deter, R. L. et al. Individualized growth assessment: conceptual framework and practical implementation for the evaluation of fetal growth and neonatal growth outcome. Am. J. Obstet. Gynecol. 218 https://doi.org/10.1016/j.ajog.2017.12.210 (2018). S656–S678 Preprint at. Wu, X. et al. Fetal weight estimation by automated three-dimensional limb volume model in late third trimester compared to two-dimensional model: a cross-sectional prospective observational study. BMC Pregnancy Childbirth 21 , (2021). Scioscia, M., Stepniewska, A., Trivella, G., De Mitri, P. & Bettocchi, S. Estimation of birthweight by measurement of fetal thigh soft-tissue thickness improves the detection of macrosomic fetuses. Acta Obstet. Gynecol. Scand. 93 , 1325–1328 (2014). Anderson, N. G., Jolley, I. J. & Wells, J. E. Sonographic estimation of fetal weight: Comparison of bias, precision and consistency using 12 different formulae. Ultrasound Obstet. Gynecol. 30 , 173–179 (2007). Aviram, A. et al. Different formulas, different thresholds and different performance - The prediction of macrosomia by ultrasound. J. Perinatol. 37 , 1285–1291 (2017). Lee, W. et al. Fetal growth parameters and birth weight: Their relationship to neonatal body composition. Ultrasound Obstet. Gynecol. 33 , 441–446 (2009). Scioscia, M. & Selvaggi, L. E. Estimation of birth weight by two-dimensional ultrasonography [2]. Obstet. Gynecol. 111 , 1215 (2008). Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6395306","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":494852069,"identity":"82b3d1ca-4a17-430a-a006-ef43cc14b5f0","order_by":0,"name":"Laura Almenar Agustí","email":"","orcid":"","institution":"La Plana University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Laura","middleName":"Almenar","lastName":"Agustí","suffix":""},{"id":494852070,"identity":"0f5e1f98-698e-476e-b54e-4adc42b9e680","order_by":1,"name":"Antoni Llueca Abella","email":"","orcid":"","institution":"Unidad Predepartamental de Medicina, Universitat Jaume I","correspondingAuthor":false,"prefix":"","firstName":"Antoni","middleName":"Llueca","lastName":"Abella","suffix":""},{"id":494852071,"identity":"07ba56d4-161d-45a9-bbe0-2e065a7855fa","order_by":2,"name":"Paula Carrasco Espí","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAt0lEQVRIiWNgGAWjYDADfgaGhAOkaZFsIFmLAdHqzdkPP/tcUVMnb3wj4eEBhoo6wlose9KMZ545dthw240EoMPOHCbCPTcYjBkb2A4kmIG0MLYR4TyDG+yfGRv+1SUYzwBp+UeEwwxu8BgzNrYxJxhIgLQ0MBPWYtmTU8zY2HfYcMaZBwkHEo4R4Rdz9uObGRu+1cnzt+ckf/hQQ4zDEEyeBIYEwhpQtLAfIEbDKBgFo2AUjEAAAAE8PC+ImKn7AAAAAElFTkSuQmCC","orcid":"","institution":"Predepartamental Unit of Medicine Faculty Of Health Sciences. Jaime I University.","correspondingAuthor":true,"prefix":"","firstName":"Paula","middleName":"Carrasco","lastName":"Espí","suffix":""},{"id":494852072,"identity":"bbc091e3-3af5-461b-99c1-3fc6faee5da2","order_by":3,"name":"Eva Maria Moya Artuñedo","email":"","orcid":"","institution":"La Plana University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Eva","middleName":"Maria Moya","lastName":"Artuñedo","suffix":""}],"badges":[],"createdAt":"2025-04-07 14:38:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6395306/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6395306/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88523098,"identity":"a17eef2e-c376-41a0-b90f-482cbdd5ab10","added_by":"auto","created_at":"2025-08-07 10:02:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":318217,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot showing the linear regression line between estimated foetal weights by formulas (Lee, Scioscia and Hadlock) and actual birth weight. Note: the dotted line shows the perfect relationship; R\u003csup\u003e2\u003c/sup\u003e: coefficient of determination as a measure of the goodness of fit\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6395306/v1/e4e2aa31a849d0abfee47707.png"},{"id":88525974,"identity":"7be934b6-0011-4359-ae98-e1aa8a11213c","added_by":"auto","created_at":"2025-08-07 10:26:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1162150,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6395306/v1/58c628e8-588d-42d7-bd6a-be09b2f49cab.pdf"},{"id":88523101,"identity":"386d3987-5db8-497d-a998-6a4983e40418","added_by":"auto","created_at":"2025-08-07 10:02:07","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":115130,"visible":true,"origin":"","legend":"","description":"","filename":"modelinformedconsentstudy.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6395306/v1/ea34b8fe9fa1ad6f15288c23.pdf"},{"id":88523113,"identity":"74478c51-839e-492a-853a-47b42856cb4a","added_by":"auto","created_at":"2025-08-07 10:02:09","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":31352011,"visible":true,"origin":"","legend":"","description":"","filename":"Informedconsentpatient1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6395306/v1/76bf169a98bf1971dd87812d.pdf"},{"id":88523114,"identity":"28659a09-0bf7-493d-a770-1afb95a16890","added_by":"auto","created_at":"2025-08-07 10:02:09","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":29582769,"visible":true,"origin":"","legend":"","description":"","filename":"Informedconsentpatient2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6395306/v1/37471f52fe929a922c67b73a.pdf"},{"id":88523115,"identity":"1fcdbb8e-8916-4d0b-bcf6-05a6af32de12","added_by":"auto","created_at":"2025-08-07 10:02:09","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":28142375,"visible":true,"origin":"","legend":"","description":"","filename":"Informedconsentpatient3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6395306/v1/87b0e689ac02ee4bb3e1724e.pdf"},{"id":88523127,"identity":"46b48816-5f33-448c-b3cb-d52e5f48f658","added_by":"auto","created_at":"2025-08-07 10:02:10","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":30659363,"visible":true,"origin":"","legend":"","description":"","filename":"Informedconsentpatient4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6395306/v1/c04fa5cc2294ce620e84f29c.pdf"},{"id":88523116,"identity":"0eef1067-61cd-4a15-b187-d59fb86d2d3c","added_by":"auto","created_at":"2025-08-07 10:02:09","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":19013213,"visible":true,"origin":"","legend":"","description":"","filename":"StudydatabaseFetalweightestimationevaluationofdifferentmethods2025.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6395306/v1/f3533dde76e163149d01a2d1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Fetal weight estimation, evaluation of different methods","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe estimated fetal weight is considered a key parameter for monitoring fetal growth and allows for early diagnosis of normal and abnormal patterns such as fetal growth restriction (FGR) or macrosomia that are strongly associated with an increased risk of adverse outcomes at birth \u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Growth-restricted fetuses have increased risk for admission to neonatal intensive care units, low Apgar scores, neurological injury, stillbirth or early neonatal death \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Fetal macrosomia is associated with an increased risk of shoulder dystocia, or asphyxia, as well as maternal complications, such as prolonged labor, instrumental delivery, cesarean section, postpartum hemorrhage or uterine rupture \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eA reliable fetal weight estimation of birthweight is important to reduce adverse outcomes and to optimize clinical decisions during gestational follow-up, the timing and delivery route and therapeutic interventions. In addition, an accurate measurement of the gestational age of fetal growth may facilitate an adequate diagnosis of fetuses with growth disorders, avoiding iatrogenic medicalization of the pregnancy \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. However, the accuracy of prenatal weight estimation remains a challenge especially when fetal growth is suboptimal \u003csup\u003e\u003cspan additionalcitationids=\"CR11 CR12 CR13\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eAt present, ultrasonography is the best method for dating gestation, monitoring fetal growth during pregnancy and diagnosing fetal growth disorders \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. There are numerous regression formulas for the estimation of fetal weight via two-dimensional (2D) ultrasound, consisting of different combinations of fetal biometric measurements including head circumference (HC), biparietal diameter (BPD), femur length (FL) and abdominal circumference (AC). \u003csup\u003e\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn addition, human fetuses have the greatest proportion of body fat among mammalian species and an enormous capacity for altering this body compartment as a result of intrauterine growth \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Accurate evaluation of soft tissue masses could provide additional insight into physiological adaptations to intrauterine growth disturbances.\u003c/p\u003e\u003cp\u003ePostnatal investigations suggest that soft tissue assessment should provide additional insight into generalized fetal nutritional status \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eDespite their clinical value, accurate estimation of fetal weight and prediction of macrosomia or growth retardation are challenging, with significant margins of error for both clinical estimates and routine two-dimensional (2D) ultrasound biometry, most of which are present at the extremes of fetal weight. \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eIf a measurement of limb soft tissue volume is added to 2-dimensional (2D) fetal biometry, it could substantially improve the accuracy of these estimates. Conventional fetal weight prediction models typically include 2D ultrasound measurements of the head circumference, abdominal circumference, and femur diaphysis length \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, regardless of soft tissue development such as what could be provided with fractional limb volume (FLV). \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan additionalcitationids=\"CR25 CR26\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy design and subjects\u003c/h2\u003e\u003cp\u003eWe conducted a prospective study of fetal biometry via 2D and 3D ultrasound at the Gynecology and Obstetrics Unit of La Plana University Hospital in Castell\u0026oacute;n, a Mediterranean region in eastern Spain. The study population included pregnant caucasian women (n\u0026thinsp;=\u0026thinsp;236) who were invited to participate before the 40th week of scaning between January 2019 and October 2022. The inclusion criteria were: women 18 years or older with uncomplicated singleton pregnancies monitored at the La Plana Hospital, sonographic evidence of normal amniotic fluid, and fetuses with no structural or chromosomal alterations, who presented as cephalic and delivered between 37\u0026thinsp;+\u0026thinsp;0 and 41\u0026thinsp;+\u0026thinsp;6 weeks of gestation. We excluded women with psychiatric or cognitive pathology that prevented understanding and consent to the study, women taking drugs or other teratogenic products during gestation or who had induced or spontaneous abortions or fetal deaths.\u003c/p\u003e\u003cp\u003eOf the 236 women recruited, 208 attended the last ultrasound scan and were followed up until delivery. Fetuses that gave birth more than 10 days after the ultrasound scan (n\u0026thinsp;=\u0026thinsp;5) and fetuses that lacked documentation of all biometric measurements in the last scan and birth weight (n\u0026thinsp;=\u0026thinsp;4) were excluded, the final population included in this study included 199 participants.\u003c/p\u003e\u003cp\u003eInformation about maternal age at conception, previous gestation, maternal prepregnancy body mass index (BMI [kg/m\u003csup\u003e2\u003c/sup\u003e]), maternal weight gain during pregnancy, maternal active smoking during pregnancy and the presence of gestational diabetes was collected via a questionnaire during the scan visit.\u003c/p\u003e\u003cp\u003e The study was approved by the Ethics and Clinical Research Committee of the Hospital de La Plana in October 2018. Written informed consent for participation and data usage was obtained from all participants and from the parents or legal guardians of each fetus/newborn included in the study. All experiments were performed according to the relevant guidelines.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eUltrasonic data collection\u003c/h3\u003e\n\u003cp\u003eWomen had 2D and 3D ultrasonography scans for estimation of fetal weight between 37\u0026thinsp;+\u0026thinsp;0 and 41\u0026thinsp;+\u0026thinsp;6 weeks of gestation within 10 days of delivery. Gestational age at the time of sonographic evaluation was calculated by the date of the onset of the last menstrual period confirmed and modified by first trimester ultrasound data if the discrepancy between them exceeded 5 days \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe estimated fetal weight was calculated according to different regression models published in the literature and presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e to compare their accuracy in this study. These formulas include standard 2D sonographic parameters, i.e (BPD, HC, AC and FL) (18), the 3D automated fractional thigh volume (TVol) described by Lee\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e and the 2D soft tissue of the femur (STT) described by Scioscia\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFormulas used for sonographic fetal weight estimation\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHadlock II (1985)\u003c/p\u003e\u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e- Required ultrasound parameters: BPD, HC, AC, FL\u003c/p\u003e\u003cp\u003e- log\u003csub\u003e10\u003c/sub\u003e EFW\u0026thinsp;=\u0026thinsp;1.3596\u0026thinsp;+\u0026thinsp;0.0064(CC)\u0026thinsp;+\u0026thinsp;0.0424(CA)\u0026thinsp;+\u0026thinsp;0.174(FL)\u0026thinsp;+\u0026thinsp;0.00061(DBP)(CA) -0.00386(CA)(FL)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLee (2004)\u003c/p\u003e\u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e- Required ultrasound parameters: BPD, CA, TVol\u003c/p\u003e\u003cp\u003e- Ln BW = -0.8297\u0026thinsp;+\u0026thinsp;4.0344 (ln BPD) -\u003c/p\u003e\u003cp\u003e0.7820 (ln BPD)2\u0026thinsp;+\u0026thinsp;0.7853 (ln AC)\u0026thinsp;+\u0026thinsp;\u003c/p\u003e\u003cp\u003e0.0528 (ln TVol) 2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eScioscia (2008)\u003c/p\u003e\u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e- Required ultrasound parameters: STT, FL\u003c/p\u003e\u003cp\u003e- EFW = -1687.47 + (54.1 \u0026times; FL) + (76.68 \u0026times; STT)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"2\"\u003eNote: EFW: Estimated foetal weight, BPD: Biparietal diameter, HC: Head circumference, AC: Abdominal\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eMeasurement of BPD and HC was obtained by means of a cephalic transverse plane in which the midline was observed, allowing visualization of the interhemispheric fissure and the cavum of the septum pellicidum \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. The assessment of the CA was based on an abdominal cross section including the umbilical vein and the origin of the gastric chamber \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. At the level of the extremities we obtained the FL, STT and TVol in the longitudinal plane of the femur closest to the transducer where the femoral diaphysis was observed \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eUltrasound scanning was performed with a General Electrics Voluson E6 ultrasound scanner, which has a probe capable of calculating volumes. Each scanning sweep lasted approximately 10\u0026ndash;15 minutes and the volume data were stored in removable digital media for later analysis. All fetal measurements were performed by an experienced physician in obstetric ultrasound.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePostnatal assessment\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePerinatological data were collected using the newborn identification register and medical records were reviewed. Birth weights were recorded at delivery and were stratified as follows: \u0026lt; 3000 g, 3000\u0026ndash;3499 g, 3500\u0026ndash;3999 g and \u0026gt;\u0026thinsp;4000g. Additionally weeks of gestation, delivery type (eutocic, instrumental or caesarean), days between the last scan and delivery and newborn sex were documented.\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eFirst, a descriptive analysis was conducted for maternal, obstetrical and fetal characteristics in the overall sample of study participants and according to birth-weight classification.\u003c/p\u003e\u003cp\u003eCategorical variables are expressed as frequencies (percentages). Continuous variables were expressed as the mean (standard deviation [SD] as dispersion measure) when they followed a normal distribution. Non-normally distributed continuous variables are reported as medians (interquartile range [IR]). Normality of continuous variables was measured by examining density plots and histograms.\u003c/p\u003e\u003cp\u003eThe correlation between estimated fetal weight and birth weight was estimated using the Spearman correlation coefficient. The linear regression line between each of the prediction methods and the actual birth weight and the goodness of fit with R\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e were also obtained and plotted in scatter diagrams. The performance of the three formulas for estimating fetal weight was assessed by accuracy (systematic error) and precision (random error) as previously proposed\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Sistematic error was calculated from the signed mean percent difference with the birth weight using the following formula: estimated weight \u0026ndash; BW)/ BW \u0026times; 100. Random error was calculated as the standard deviation of percent differences. The proportion of neonates whose estimated fetal weight was within 5% and 10% of BW were also calculated. For each regression model the mean square error (MSE) was computed as an unbiased estimator of the population variance \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered to indicate statical significance. All analyses were performed with R version 4.4.3.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eDemographic and obstetrical characteristics of the study population (n\u0026thinsp;=\u0026thinsp;199) are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The mean (SD) age of pregnant women participating in this study was 33.0 (4.8) years with an average number of previous pregnancies of 2.1 (1.0). The mean BMI was 24.4 (4.6) kg/m\u003csup\u003e2\u003c/sup\u003e and mean weight gain during pregnancy was 12.1 (6.6) kg, with no differences found between pregnant women according to birth weight of the newborn classification. Approximately 15.7% of mothers smoked during pregnancy. Nineteen cases (9.6%) of gestational diabetes have been detected. The majorityof deliveries were eutocic (69.5%), 12.6% instrumented and 17.9% by cesarean section. The percentage of cesarean section deliveries was higher in mothers with newborns weighing more than 4000 g at birth (43.8%). Pregnant women were scanned with a mean (SD) of 5.6 (3.2) days before delivery. About 51,3% of neonates were female and 48.7% male and were delivered at a mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD gestational age of 39.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6 weeks. Female sex was higher among neonates born at less than 3000 g (75%). The overall mean (SD) of birth weight was 3,482 (379) g (range 2660\u0026ndash;4785 g).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive measures of the study population and according to the birth weight classification\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;199\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt; 3000 g\u003c/p\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;16\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3000\u0026ndash;3499 g\u003c/p\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;95\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3500\u0026ndash;3999 g\u003c/p\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;72\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;4000 g\u003c/p\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;16\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep-value\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaternal characteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, mean (SD) years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33.0 (4.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32.0 (5.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32.8 (4.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e33.1 (5.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e35.4 (3.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.173\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrevious gestations, mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.1 (1.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.5 (0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.0 (1.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.2(0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.3(1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.050\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePre-pregnancy height, mean (SD) kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.64 (0.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.64 (0.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.63 (0.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.65 (0.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.66 (0.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.302\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePre-pregnancy BMI, mean (SD) kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24.4 (4.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.6 (4.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.8 (4.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e24.0 (4.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e24.7 (3.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.677\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeight gain, mean (SD) (kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.1 (6.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.8 (2.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.5 (8.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12.8 (4.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15.5 (5.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.300\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking in pregnancy (yes), n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31 (15,7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (18,8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18 (18,9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10 (13,9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.282\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGestational diabetes mellitus (yes), n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19 (9.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (6.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10 (10.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7 (9.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1 (6.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.976\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeeks of gestation, mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39.9 (0.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39.9 (0.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39.9 (0.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e39.9 (0.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e39.8 (0.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.926\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDelivery type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.119\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEutocic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e132 (69.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9 (69.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e66 (72.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e50 (71.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7 (43.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInstrumented\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24 (12.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (23.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10 (11.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9 (12.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2 (12.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCaesarean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34 (17.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (7.69%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15 (16.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11 (15.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7 (43.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDays between last scan and delivery, mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.6 (3.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.6 (2.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.3 (3.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.2 (3.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.4 (2.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.117\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNewborn\u0026rsquo;s characteristics\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBirthweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emean (SD) g\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3482 (379)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2880 (103)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3274 (136)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3712 (142)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4291 (251)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian (IR) g\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3430 (3238;3740)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2900 (2848;2962)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3280 (3190;3400)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3725 (3585;3821)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4172 (4092;4520)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex (female), n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e102 (51.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (75.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e53 (55.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e31 (43.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6 (37.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: n sample size; SD standard deviation; IR interquartile range; BMI body mass index; Continuous variables are presented as mean (SD); categorical values are n (%). \u003csup\u003ea\u003c/sup\u003ep-value from the chi-square test (categorical variables),ANOVA test when comparing means and Kruskall-Wallis test when comparing medians.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eCorrelation analysis between the estimated weights using the different formulas and birth weight is summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. A positive correlation was observed in all cases but the estimates using Hadlock and Lee models were higher (r\u0026thinsp;=\u0026thinsp;0.68 IC95% [0.65;0.75] and (r\u0026thinsp;=\u0026thinsp;0.67 IC95% [0.59;0.75], respectively) than that observed using the Scioscia model (r\u0026thinsp;=\u0026thinsp;0.29 IC95% [0.15;0.42]). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the relationships between the estimated weights and birth weight in a scatter plot including the coefficient of determination R\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Linear regression line better fits the actual birth weight for the Hadlock and Lee models than for the Scioscia model.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePerformance summary of formulas that estimate foetal weight (Hadlock et al, Lee et al and Scioscia et al) with respect to birth weight.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;199\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt; 3000 g\u003c/p\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;16\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3000\u0026ndash;3499 g\u003c/p\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;95\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3500\u0026ndash;3999 g\u003c/p\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;72\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;4000 g\u003c/p\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;16\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCorrelation with BW (r, 95% CI)\u003c/b\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHadlock et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.68 (0.65;0.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.03(-0.49;0.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.44(0.26;0.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.42(0.20;0.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.40(-0.14;0.75)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLee et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.67 (0.59; 0.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.21(-0.34;0.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.32(0.12;0.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.30(0.06;0.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.20(-0.34;0.64)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eScioscia et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.29 (0.15;0.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.13(-0.61;0.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.13(-0.08;0.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.11(-0.14;0.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.38(-0.75;0.18)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSystematic error ( random error)\u003c/b\u003e\u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHadlock et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.3 (8.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.8 (7.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.0 (6.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.5 (7.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-4.0 (6.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLee et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-9.3 (7.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-2.3 (5.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-8.1 (7.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-11.4 (6.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-14.4 (9.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eScioscia et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.5 (12.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.3 (11.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.0 (11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.3 (9.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-9.8 (9.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAbsolute mean error, n(% of cases)\u003c/b\u003e\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHadlock et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e85 (42.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (18.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e37 (38.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e37 (51.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8 (50.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u0026ndash;10%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e59 (29.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (6.25%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29 (30.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e24 (33.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5 (31.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;10%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55 (27.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (75.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29 (30.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11 (15.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3 (18.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLee et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e56 (28.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 (68.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31 (32.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14 (19.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u0026ndash;10%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50 (25.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (25.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27 (28.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16 (22.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3 (18.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;10%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e92 (46.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (6.25%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e37 (38.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e41 (57.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13 (81.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eScioscia et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e62 (31.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (13.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27 (28.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e28 (39.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5 (33.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u0026ndash;10%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e44 (22.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (6.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18 (18.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22 (31.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3 (20.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;10%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e90 (45.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (80.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50 (52.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21 (29.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7 (46.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRMSE\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHadlock et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e303.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e416.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e307.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e261.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e327.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLee et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e446.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e166.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e368.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e495.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e740.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eScioscia et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e468.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e665.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e482.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e352.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e603.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: BW: birth weight; RMSE: root mean square error\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003ea\u003c/sup\u003er: Spearman correlation coefficient; CI: confidence intervals\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003eb\u003c/sup\u003eSystematic error was calculated from the signed mean percent difference as estimated weight \u0026ndash; BW)/BW \u0026times; 100; random error was calculated as the standard deviation of percent differences.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003ec\u003c/sup\u003eAbsolute frequency and proportion of neonates with fetal weight predictions within \u0026lt;\u0026thinsp;5%, 5\u0026ndash;10%, \u0026gt;10% of actual BW\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe systematic errors, random errors, RMSE and prediction rates within, 5%, 5\u0026ndash;10% and over 10% are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In general, systematic and random errors were lower for Hadlock than for Lee and Scioscia models. For the Hadlock model the mean systematic error was 4.3% and the random error was 8.0%. For newborns with weights\u0026thinsp;\u0026lt;\u0026thinsp;4000 g, the predicted birth weights were slightly overestimated (range, 0.5; 12.8%) and for the 4000 g threshold, the estimated birth weights were slightly underestimated (-4.01%). Random errors were similar for all the subgroups (range, 6.4\u0026ndash;7.2%). For the Lee model the mean systematic error was \u0026minus;\u0026thinsp;9.3% and the random error was 7.9% which was negative for all groups suggesting an underestimation of birth weight. However, systematic error was lower for the neonates in the \u0026lt;\u0026thinsp;3000 g group than for those in the higher weight groups, with the highest error observed in the group weighing\u0026thinsp;\u0026ge;\u0026thinsp;4000 g (range, -2.3; -14.4%). Random error was also lower in the group weighing\u0026thinsp;\u0026lt;\u0026thinsp;3000 g and higher in the group weighing 4000 g or more (range, 5.3; 9.1%). For the Scioscia model the mean systematic error was 5.5% and random error 12.9%. The highest systematic error was observed in the \u0026lt;\u0026thinsp;3000 g group (20.3%) which improved in the 3500\u0026ndash;3999 g group (-0.3%). It was positive in neonates weighing less than 3500 g and negative when the birth weight was higher. Random errors were similar for all subgroups (range, 9.1\u0026ndash;11.9%).\u003c/p\u003e\u003cp\u003eFor neonates with a birth weight\u0026thinsp;\u0026lt;\u0026thinsp;3000 g, the lowest percentage of systematic and random error was observed for the Lee model (-2.3 [5.3]%) and the highest for the Scioscia model (20.3 [11.9]%). For newborns having a birth weight between 3000 g and 3499 g, estimated birth weight using Hadlock model had slightly smaller systematic and random error (7.0 [6.4]%) than the other two models. For neonates with a birth weight between 3500 g and 3999 g, the systematic error was considerably reduced for the Hadlock and Scioscia models although the random error was similar for all three models (range, 6.8\u0026ndash;9.6%). When birth weight exceeded 4000 g, the lowest systematic and random error was observed for Hadlock model (-4.0 [6.4]%) and increased to -9.8 (9.1)% and \u0026minus;\u0026thinsp;14.4 (9.1)% respectively for Scioscia and Lee models.\u003c/p\u003e\u003cp\u003eFor the Hadlock model, the proportion of neonates for whom the percentage difference between estimated and observed birthweight was less than 5% and between 5\u0026ndash;10%, were higher (42.7% and 29.6%, respectively) than for the Lee and Scioscia models.\u003c/p\u003e\u003cp\u003eHowever, the prediction rate within 5% and between 5\u0026ndash;10% of birth weight for the Lee model was higher (68.8% and 25%, respectively) than those for Hadlock and Scioscia models in newborns with less than 3000 g at birth. However, the percentage is lower in newborns over 3000 g at birth, reaching 0% in newborns of 4000 g or more. In newborns over 3000 g, the method described by Hadlock obtained a greater number of cases with less than 5% of difference error between estimated and observed birthweight. In newborns over 3500 g this method showed a difference of less than 5% in more than 50% of the cases.\u003c/p\u003e\u003cp\u003eRMSE values for Hadlock model were lower compared to Lee and Scioscia models for all newborns and those with birth weight 3000 g or more. The Lee model provided lowest RMSE value For neonates with a birth weight\u0026thinsp;\u0026lt;\u0026thinsp;3000 g and highest for neonates with a birth weight between 3500 g and 3999 g and over 4000 g.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eBirth weight is a well-established predictor of neonatal and child morbidity and mortality \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003eTherefore, obstetricians should be able to obtain the most valid fetal weight estimations for guiding clinical decisions as a routine part of prenatal care. \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eEstimated fetal weight is a composite parameter that can be calculated by different methods. In routine clinical practice it is usually assessed with the formula described by Hadlock\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, in wich only skeletal measurements are used (DBP, CC, AC, FL) and does not include soft tissue parameters. Commonly used fetal ultrasound mesurements for prediction of estimation fetal weight can only predict birth weight with modest accuracy, and these measurements are limited for prediction of percentage of neonatal fat, wich accounts 46% of the variance in birth weight. \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eSeveral investigators have used 3D ultrasonography to demonstrate a significant correlation between fetal limb volume and birth weight \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eScioscia published a prospective study evaluating the performance of an equeation based on the linear measurement of the soft tissue above the external fetal femur. Their new formula showed a reduced standard desviation, which means a lower internal error in prediction. \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e Lee conducted a prospective, cross-sectional study of 164 pregnant women with singleton fetuses in the second and third trimesters of pregnancy, were compared the accuracy of fetal weigth estimation using fractional thigh volumen with a Hadlock\u0026rsquo;s formula. This publication showed that the prediction model that incorporated fractional thigh volume correctly classified a greater proportion of EFW within 5% of BW compared with the modified Hadlock model, (16)\u003c/p\u003e\u003cp\u003eSubsequently, Lee et al examined more 300 pregnancies in a prospective multicenter study they concluded that the inclusion of automated FLV measurements with conventional 2-dimensional biometry was generally associated with improved weight predictions, which has also been reported by other authors \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eOur results show that all three ultrasound formulas obtained a positive correlation with the real birth weight. The most accurate results were obtained with the weight estimation method described by Hadlock (r\u0026thinsp;=\u0026thinsp;0.68) but very similar to that described by Lee (RHO 0.67). In fetuses under 3000 g the method described by Lee, using soft tissue measured as fractional volume of the thigh, has obtained more accurate estimates. The size of these fetuses is similar to the mean of 34\u0026ndash;36 week fetuses, which are those evaluated in previous studies.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eThis finding in fetuses over 3000 g is different from those of previous studies, in which the estimated values obtained using soft tissue parameters were superior \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eA practical challenge for using FLV is the time it takes to manually trace limb circumferences via trackball in routine clinical practice.\u003c/p\u003e\u003cp\u003eThe sonographic data were saved and analyzed on delay with the 4Dview software ang through the TUI application. The central axis of the thigh is marked (following the femoral diaphysis) and the central 50% is automatically subdivided into 5 equidistant slices. The areas of each of these slices were manually plotted to obtain the fractional volume \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e .\u003c/p\u003e\u003cp\u003eAlthough possible to perform automated FLV measurements to provide a faster and more practical means for adding a soft tissue component to the fetal weight estimation process. Reproducible computer assisted FLV measurements are approximately 5 times faster than manual methods, which otherwise require 2.5 to 3.0 minutes to be completed. \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eAll our ultrasound scans had been performed approximatelly 40th weeks of gestation, later than most of the studies evaluating different methods of fetal weight estimation examine fetuses of lower gestacional age. \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eOnly in fetuses under 3000 g the method described by Lee obtained more accurate estimates. These findings agreed with those previously presented, as the fetuses are the smallest in size and it\u0026rsquo;s possible to obtain the echographic planes more easily and we could better delimit the fetal parts.\u003c/p\u003e\u003cp\u003eIs possible that the older gestational age, under normal conditions, and the larger and heavier the fetus is, the more difficult is to obtain the correct ultrasound planes without putting excesive pressure on the fetal parts. In the last weeks of gestation differentiatig the fetal parts renders it difficult to obtain accurate values.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eTheses circumstances may have interferred the discrepancies with some studies published, since we haven\u0026rsquo;t obtained a benefit in the accuracy of fetal weight estimation by adding soft tissue parameters.\u003c/p\u003e\u003cp\u003eThe main strengths of our work include prospective data collection from a low-risk population, we have analyzed fetal weights with little margin to delivery, and all ultrasound estimates of late gestation were taken by the same obstetrician at the same ultrasound examination.\u003c/p\u003e\u003cp\u003eThe main limitation has been the relative paucity of research participants with fetuses with abnormal growth, due to the selection of our low-risk population many of them were discarded before recruitment, as many of them had already been indicated for termination. Therefore, the percentage of fetuses with growth below the 3rd percentile are the patients who have been overestimated in previous biometries, having a small sample of this population where it is trully more important in clinical practice to be more accurate.\u003c/p\u003e\u003c/div\u003e\u003cp\u003eIn conclusion, our results are in line with current literature and recommendations. The most accurate estimate of fetal weight was obtained with the formula described by Hadlock for the total population.\u003csup\u003e\u003cspan lang=\"EN-US\"\u003e20\u003c/span\u003e\u003c/sup\u003e The usefulness of volume or fetal soft tissue parameters has been observed in the subgroup of smaller fetuses, where it is necessary to be more precise at the clinical level, thus this option is promising because it is more accurate in our routine clinical practice and generates less iatrogenesis.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv id=\"AGS1\" class=\"AbbreviationGroupSection\"\u003e\u003cdiv class=\"Heading\"\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e- FGR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e- Fetal growthrestriction\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e- BW\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e- Birth weight\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e- EFW\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e- Estimated fetal weight\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e- HC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e- Head circumference\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e- BDP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e- Biparietal diameter\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e- FL\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e- Femur length\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e- AC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e- Abdominal circumference\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e- FLV\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e- Fractional limb volume\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e- STT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e- Soft tissue in the medial thigh area.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eWe haven\u0026rsquo;t obtained external financing\u003c/p\u003e\u003cp\u003e\u003cb\u003eConflic of interest\u003c/b\u003e: None declared.\u003c/p\u003e\u003cp\u003e\u003cb\u003eData Availability Statement\u003c/b\u003e: The data is provided in the manuscript or in the supplementary information files, For more information on the data, please contact the corresponding author or first author. If you need the data to be published in any entity, please let us know.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eE.M. Has designed the study to be carried out and has selected the target population on which to perform it. Has reviewed and contributed in the sampling of patients and in the revision of the manuscript.P.C. Has directed and carried out the main part of the statistical study, has written the statistical method part of manuscript, has elaborated the tables and has reviewed the entire manuscript.L.A. Has carried out the literature review and designed the study, collected most of the data for the study (sonographic data, all the data on the pregnant women an the all perinatal data), selected the patients who met the informed criteria and explained the informed consent. L.A. Has prepared de database, analyzed the results and has written a large part of the manuscript. A. L. Has reviewed the study design and made modifications, reviewed the data collected and revised the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement:\u003c/h2\u003e\u003cp\u003eWe would like to thank the La Plana University Hospital for the possibility of carrying out this study, and the University Jaume I (UJI) for their collaboration.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eArenas Ram\u0026iacute;rez, J. \u0026amp; GU\u0026Iacute;A DE LA EXPLORACI\u0026Oacute;N ECOGR\u0026Aacute;FICA DE III TRIMESTRE Gu\u0026iacute;a de Asistencia. Pr\u0026aacute;ctica de La Secci\u0026oacute;n de Ecograf\u0026iacute;a Obst\u0026eacute;trico-Ginecol\u0026oacute;gica de La SEGO, Publicada En Octubre de (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSimcox, L. E., Myers, J. E., Cole, T. J. \u0026amp; Johnstone, E. D. 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S656\u0026ndash;S678 Preprint at.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWu, X. et al. Fetal weight estimation by automated three-dimensional limb volume model in late third trimester compared to two-dimensional model: a cross-sectional prospective observational study. \u003cem\u003eBMC Pregnancy Childbirth\u003c/em\u003e \u003cb\u003e21\u003c/b\u003e, (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eScioscia, M., Stepniewska, A., Trivella, G., De Mitri, P. \u0026amp; Bettocchi, S. Estimation of birthweight by measurement of fetal thigh soft-tissue thickness improves the detection of macrosomic fetuses. \u003cem\u003eActa Obstet. Gynecol. Scand.\u003c/em\u003e \u003cb\u003e93\u003c/b\u003e, 1325\u0026ndash;1328 (2014).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAnderson, N. G., Jolley, I. J. \u0026amp; Wells, J. E. Sonographic estimation of fetal weight: Comparison of bias, precision and consistency using 12 different formulae. \u003cem\u003eUltrasound Obstet. Gynecol.\u003c/em\u003e \u003cb\u003e30\u003c/b\u003e, 173\u0026ndash;179 (2007).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAviram, A. et al. Different formulas, different thresholds and different performance - The prediction of macrosomia by ultrasound. \u003cem\u003eJ. Perinatol.\u003c/em\u003e \u003cb\u003e37\u003c/b\u003e, 1285\u0026ndash;1291 (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee, W. et al. Fetal growth parameters and birth weight: Their relationship to neonatal body composition. \u003cem\u003eUltrasound Obstet. Gynecol.\u003c/em\u003e \u003cb\u003e33\u003c/b\u003e, 441\u0026ndash;446 (2009).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eScioscia, M. \u0026amp; Selvaggi, L. E. Estimation of birth weight by two-dimensional ultrasonography [2]. \u003cem\u003eObstet. Gynecol.\u003c/em\u003e \u003cb\u003e111\u003c/b\u003e, 1215 (2008).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Ultrasound, Soft tissue, Fractional thigh volume, Three-dimensional models, Fetal growth","lastPublishedDoi":"10.21203/rs.3.rs-6395306/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6395306/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eIntroduction\u003c/b\u003e: The accuracy of ultrasound estimation of fetal weight is influenced by the imprecision of ultrasound methods. The aim of this study is to compare the accuracy of fetal weight estimation between conventional (2D) models and those including 2D and 3D subcutaneous tissue measurements.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e: Prospective study of 199 low-risk pregnant women at the Hospital Universitario La Plana, Vila-Real, Spain. Data acquisition included the 2D and 3D parameters described by Lee, Hadlock and Scioscia.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e: All ultrasound formulas correlated positively with actual birth weight calculated by Sperman's method. Hadlock's model has the highest correlation with a different percentage\u0026thinsp;\u0026lt;\u0026thinsp;5% of 42% and Sperman's correlation (r\u0026thinsp;=\u0026thinsp;0.68).\u003c/p\u003e\u003cp\u003eIf we disaggregate the results by fetal weight categories, in newborns\u0026thinsp;\u0026gt;\u0026thinsp;3000 g, Hadlock\u0026rsquo;s method was more accurate, 50% of the fetuses had less than 5% difference calculated as mean percentage difference, in newborns\u0026thinsp;\u0026lt;\u0026thinsp;3000 g Lee\u0026rsquo;s method obtained more accurate results, 68.8% of the estimated weigths were within 5% of birth weight.\u003c/p\u003e\u003cp\u003eOverall, systematic and random errors were lower for the Hadlock models than for the Lee and Scioscia models.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e: The inclusion of fractional thigh volume in the ultrasound estimation of fetal weight improves the estimation of fetuses weighing less than 3000g. In our study Hadlock's method provided more accurate estimates of fetal weight in fetuses with normal range growth.\u003c/p\u003e","manuscriptTitle":"Fetal weight estimation, evaluation of different methods","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-07 10:02:03","doi":"10.21203/rs.3.rs-6395306/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-08-03T08:09:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-27T16:38:11+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-04-18T13:52:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-18T04:55:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-04-07T14:22:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"15a05676-c72e-47d5-8b41-ecc56555a3c1","owner":[],"postedDate":"August 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":52554014,"name":"Health sciences/Health care/Medical imaging"},{"id":52554015,"name":"Health sciences/Health care"}],"tags":[],"updatedAt":"2025-08-07T10:02:03+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-07 10:02:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6395306","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6395306","identity":"rs-6395306","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}