|
 
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| ORIGINAL ARTICLE |
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| Year : 2022 | Volume
: 29
| Issue : 1 | Page : 50-58 |
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Validity of sonographic prediction of birth weight: A study of three algorithms in a cohort of healthy pregnant women of Yoruba descent in a suburb of Lagos state, Southwest Nigeria
Cletus Uche Eze1, Kingsley Chibuike Cosmas2, Joshua Ifeanyichukwu Nwamba3, Ernest Ruto Upeh4
1 Department of Radiography, School of Health Technology, Federal University of Technology, Owerri, Imo State, Nigeria
2 Department of Radiology, Ultrasound Division, Promise Medical Center, Dopemu-Agege Road, Dopemu, Nigeria
3 Ultrasound Unit, Canaanshore Medical Diagnostic Services, No. 2 Abundance Avenue, Off Adex Shonibare Road, Iba, Lagos State, Nigeria
4 Ultrasound Unit, North Cumbria Integrated Care NHS Foundation Trust, UK
| Date of Submission | 21-Mar-2022 |
| Date of Acceptance | 29-Sep-2022 |
| Date of Web Publication | 15-Nov-2022 |
Correspondence Address:
Dr. Cletus Uche Eze
Department of Radiography, School of Health Technology, Federal University of Technology, Owerri, Imo
Nigeria
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/wajr.wajr_6_22
Background: Accurate estimation of fetal birth weight is critical in determining the delivery route and management of the neonate.
Purpose of Study: The purpose is to determine the accuracy of Hadlock IV, Campbell, and Shepard's algorithm as predictors of birth weight in a cohort of fetuses of Yoruba descent.
Materials and Methods: Fetal weight (FW) was estimated in a sample of 384 fetuses using Hadlock IV, Campbell, and Shepard's algorithm while actual birth weight (ABW) was measured. Receiver operating characteristic curves were plotted and used to determine the accuracy and sensitivity of each algorithm.
Results: Most babies (84.6%) had normal estimated fetal weight (EFW) and ABW; mean FW = 3.2 ± 0.5 kg); 10% had low weight while 5.5% were macrosomic. While EFW correlated positively and strongly with ABW, the Hadlock IV algorithm had the strongest correlation (r = 0.978). The Hadlock IV, Campbell, and Shepard's algorithms had 92%, 72%, and 56% accuracy within the tenth centile, respectively. At 95% confidence interval, Hadlock IV was the most accurate predictor of normal birth and low birth weight (area under the curve [AUC] =0.91 and 0.94, respectively). Campbell was the most accurate predictor of macrosomia (AUC = 0.89).
Conclusion: While Hadlock IV and Campbell algorithm are valid predictors, the Shepard model is a doubtful birth weight predictor among fetuses of Yoruba origin. When there is a need for absolute birth weight values, the Hadlock IV algorithm is preferred for suspected normal and low-weight babies while the Campbell model is preferred for fetuses weighing >4 kg among Yoruba fetuses.
Keywords: Accuracy, birth weight measurement, fetal weight estimation, sensitivity, sonography
How to cite this article:
Eze CU, Cosmas KC, Nwamba JI, Upeh ER. Validity of sonographic prediction of birth weight: A study of three algorithms in a cohort of healthy pregnant women of Yoruba descent in a suburb of Lagos state, Southwest Nigeria. West Afr J Radiol 2022;29:50-8 |
How to cite this URL:
Eze CU, Cosmas KC, Nwamba JI, Upeh ER. Validity of sonographic prediction of birth weight: A study of three algorithms in a cohort of healthy pregnant women of Yoruba descent in a suburb of Lagos state, Southwest Nigeria. West Afr J Radiol [serial online] 2022 [cited 2023 Sep 24];29:50-8. Available from: https://www.wajradiology.org/text.asp?2022/29/1/50/361191 |
| Introduction | |  |
Estimated fetal weight (EFW) is crucial in obstetrics because it assesses perinatal risk.[1],[2] EFW is helpful in identifying small for gestational age, large for gestational age, or macrosomic fetuses.[3] There are three categories of fetal/birth weight. These are low weight, normal weight, and excessive weight fetuses (macrosomia). Low, normal, and excessive weight fetuses weigh <2500 g, >2500–3.999 g, and >4000 g, respectively.[4] Fetuses at both extremes of weight (micro- and macrosomia, respectively) are associated with an increased risk of newborn complications.[5],[6] Fetuses that weigh more than the 90th percentile (>4000 g) are at a higher risk of limb injuries during normal delivery hence spontaneous vaginal delivery is not advisable in such cases.[7]
Methods used in estimating fetal weight (FW) in utero include the tactile method, risk factors assessment, maternal self-estimation, and ultrasonography.[8] The sonographic method is preferred over other methods because it relies on linear and/or intrauterine “objective and reproducible” planar measurement of fetal parts.[9] The sonographic method of EFW or prediction of actual birth weight (ABW) “improves fetomaternal outcome due to its objectivity and reproducibility.”[10]
The ultrasound machine uses a computer algorithm derived using measured fetal parts to estimate FW. There are many FW estimation algorithms.[11],[12],[13],[14],[15] Most FW estimation algorithms were derived from sonographically measured fetal parts such as biparietal diameter (BPD), head circumference (HC), abdominal circumference (AC), and femur length (FL).[16] Although there is a significant difference in sonographically EFW regarding ethnicity after 20 weeks of gestation,[17] and despite the fact that sonographically EFW varies according to geographic locations,[18],[19] most FW estimation algorithms were developed using data from Caucasian, Asian, or American populations.[20],[21],[22],[23],[24] Moreover, it is not advisable to use a single FW estimation algorithm at different stages of pregnancy because “fetal growth velocity is different at different gestational ages.”[25]
The Hadlock IV algorithm[12] is one of the most commonly used FW estimators in Nigeria and elsewhere.[26] This is despite the fact that divergence between EFW and ABW associated with the algorithm is significant during the early pregnancy stage.[27],[28] The sonographic method has been described as “significantly inaccurate” method of FW estimation[29] while “developing different weight prediction models suitable for different stages of pregnancy has been recommended”.[30] While 3-dimensional is reported to be more accurate than 2D method of FW estimation,[31] 2D sonography remains the most widely deployed technique in Lagos state in Southwest Nigeria. Despite significant inadequacies associated with the Hadlock IV algorithm,[27],[28] it is not just the most widely used FW estimator but the model is used for FW estimation at every stage of pregnancy in Lagos state. Therefore, in this study, we used 2D sonography to study the accuracy of Hadlock IV, Campbell's, and Shepard's algorithm as predictors of ABW in a large cohort of fetuses of Yoruba descent in Lagos state.
| Materials and Methods | | |
In this study carried out in the ultrasound unit of promise Medical center (a busy private hospital) in a suburb of Lagos state from July to December 2021, we used cross-sectional design to survey a convenience sample of 384 pregnant women. The study was approved by the Human Research Ethics Committee of the hospital where the study was carried out (Approval number is PMC 11236). Participants were recruited on a first-to-come-first-to-be recruited basis to reduce bias. Only healthy women with singleton pregnancies whose parents and grandparents were of Yoruba descent were recruited. To ensure only healthy participants were recruited, we checked through their medical antenatal history and made sure that pregnant women with diabetes mellitus, metabolic disorders, hypertension, thyroid hormone disorders, and those on some drug treatment that could affect the birth weight of the babies were not recruited. Only pregnancies of 37–42 weeks of gestation were recruited per recommendation.[4] Each participant gave written informed consent before being recruited. Only participants who were sure of the 1st day of onset of their last menstrual period (LMP) and had dating ultrasound examinations done between the 8th and 11th week of pregnancy were recruited. Dating was confirmed if the difference between gestational age estimated using LMP and that estimated using sonographic crown-rump length measurement done between 8th and 11th of gestation was <7 days per recommendation.[32]
Data collection
Demographic data such as age (as of last birthday), menstrual history, and parity were obtained from participants' hospital records and were confirmed by each participant. One of the coauthors (CKC) who then had 5 years' experience in obstetric sonography performed all sonographic examinations. The scan-delivery interval adopted for this study was 1 week per recommendation.[33] Before a participant was positioned for examination, the procedure was carefully explained to her in the presence of a female nurse (the chaperone) who remained in the ultrasound room until the end of the examination per the hospital's protocol.
A grayscale real-time ultrasound machine (ACUSON X600) with a 3.5MHz convex transducer (SIEMENS, model 10,789,636, manufactured in 2016) was used in this study. Although the ultrasound machine was <10 years old, an ultrasound equipment engineer carried out performance checks before the study began and reported that the machine was in optimal working condition. A medical physicist also examined the machine and ensured optimal resolution.
Sonographic examination
Sonographic examinations and measurements of fetal parameters were performed using standardized protocols already described.[34],[35],[36] To commence examination, the patient laid supine on the examination couch and exposed her abdomen from the xiphisternum to the pubic symphysis after which ultrasonic gel was liberally applied to the exposed portion.[37]
Measurement of biparietal diameter and head circumference
For BPD, a transverse scan image of the fetal skull that showed a midline echo of the falx cerebri and which also showed the thalamus and the cavum septum pellucidum in the midline [Figure 1] was obtained. The cursor was placed at the outer aspect of the near side echo close to the transducer and then moved using the trackball to the inner aspect of the far side echo. A line drawn at the right angle to the midline echoes from the outer to inner aspects of the two temporoparietal bones representing the BPD. HC, on the other hand, was measured from the same BPD image as the perimeter of the fetal skull [Figure 1]. | Figure 1: Sonogram showing plane of measurement for fetal head measurements. AB = occipitofrontal diameter; CD = Biparietal Diameter; ABCD = Head Circumference
Click here to view |
Measurement of abdominal circumference
Using a transverse approach, the probe was maneuvered to obtain an image of the fetal abdomen that showed the fetal liver, stomach, and the left portion of the umbilical vein. AC was measured by placing the cursor crosshead at the outermost aspect of the fetal abdomen at the level of the fetal liver from where the circumference of the fetal abdomen was traced [Figure 2]. | Figure 2: Sonogram showing the measurement of fetal abdominal circumference. GHIJK = Abdominal Circumference (33.5 cm) corresponding to 37 weeks and 3 days gestational age
Click here to view |
Measurement of femur length
Still from a transverse plane, the transducer was moved caudally till fetal iliac bones were demonstrated at which point a part of the femur was also seen as a bright echo. After that, the transducer was deftly maneuvered till the full length of the femur was demonstrated. To accurately measure FL, the cursor was placed at the center of the U-shape at one end of the femoral bone and a line was drawn to join one mid-point of the U-shape at one of the femurs to the other [Figure 3]. | Figure 3: Sonogram showing the measurement of fetal femur length. EF = Femur length (7.27 cm) corresponding to 37 weeks and 3 days gestational age
Click here to view |
Sonographic estimation of gestational age (GA) and fetal weight
The ultrasound machine used in this study (ACUSON X600) has drop-down option that enabled the selection of an algorithm. The drop-down EFW formula was used to select the required formula. Each patient was examined using Hadlock IV, Campbell's, and Shepard's algorithm in turn. After the measurement of BPD, AC, and FL, the machine automatically computed the mean gestational age (GA) and EFW. These means were automatically stored in the memory of the ultrasound machine once the sonographer pressed the summary button. The use of the drop-down option to select an algorithm one after the other ensured that the same measurements were used for each fetal parameter in all 3 formulae.
Measurement of actual birth weight
An experienced midwife weighed each baby immediately after birth after being cleaned (but before the baby's first feeding) using an electronic baby weighing machine (Pese-Bebe Villard Electronic baby scale 318.61 of Villard Medical Ltd; Le Mas, France; 2016). The weight of each baby was recorded to the nearest gram (g).
Method of data analysis
We managed our data using Statistical Package for Social Sciences (SPSS) computer software version 17.0 for Windows (SPSS Inc.; Chicago, Illinois, USA). Before data analysis commenced, we used the Kolmogorov–Smirnov test to establish the normality of continuous data. After that, we computed descriptive statistics such as mean, standard deviation, and standard error of prediction of ABW. We used One-way analysis of variance and Spearman's Correlation test to compare continuous variables in line with a previous study.[34] Using recorded ABW as the gold standard criterion, we plotted receiver operating characteristic (ROC) curves and used their operating characteristics (sensitivity, specificity, and area under the curve [AUC]) to determine the validity of each algorithm [Table 1] as a predictor of ABW. Results were considered statistically significant at P < 0.05.
| Results | | |
The mean age of the women in our sample was 28.0 ± 6.6 years. Women within the 20–40 years age range constituted 30.2% of our sample. There was no statistically significant relationship between maternal age and ABW (P = 0.06). Multiparous women constituted 60.9% of our sample; the mean height and weight of the women were 1.6 ± 0.1 m and 75.5 ± 8.9 kg, respectively; 42.2% of the sample weighed between 70 and 80 kg; most women in the sample were of normal weight (mean body mass index [BMI] =28.4 ± 2.4 kg/m2) and 2% were moderately obese. The majority of the women (25.8%) delivered their babies between the 37th and 40th week [Table 2]. | Table 2: Distribution of estimated predicted estimated fetal weight and actual birth weight according to maternal body mass index and delivery week
Click here to view |
All three algorithms detected an increase in FW between the 37th and 42nd week of gestation but the Hadlock IV algorithm showed the most uniform increase in FW [Table 2]. The difference in mean EFW and ABW between the 37th and 42nd week of delivery was statistically significant (P = 0.003).
The mean ABW was 3.2 ± 0.5 kg; most fetuses (84.6%) had normal EFW and ABW, 10% had low weight while 5.5% were macrosomic. EFWs correlated positively and strongly with ABW but the Hadlock IV algorithm had the strongest correlation [r = 0.978; [Table 3]].
Within the tenth centile, Hadlock IV had 92% accuracy in predicting ABW, Campbell had 72% and Shepard's algorithm had 56% accuracy. At 95%, Hadlock IV was the most accurate predictor of normal birth weight as evidenced by the AUC of 0.91 [Table 4] of the ROC curve [Figure 4]; Hadlock IV was also the most accurate predictor of low birth weight as shown by AUC = 0.94 [Table 5] and [Figure 5] while Campbell was the most accurate predictor of macrosomia as evidenced by AUC = 0.89 [Table 6] and [Figure 6]. | Figure 4: ROC curve for ABW ≥2.5 kg and <4 kg (Normal BW), Note that at 95% confidence level (0.86–0.95), Hadlock IV had the highest sensitivity for estimating normal birth weight. With an area under the curve of 0.91, standard error of 0.03 and a sensitivity 86% Hadlock IV had the highest accuracy in predicting actual birth weight within 2.5–4 kg. ROC – Receiver operating characteristic; ABW – Actual birth weight; BW – Birth weight
Click here to view |
 | Figure 5: ROC curve for low birth weight (ABW <2.5kg). Note that at 95% confidence level (0.90–0.98), Hadlock IV had the highest sensitivity for estimating low birth weight. With an area under the curve of 0.94, standard error of 0.03 and a sensitivity 92% Hadlock IV had the highest accuracy in predicting low birth weight <2.5 kg. ROC – Receiver operating characteristic; ABW – Actual birth weight
Click here to view |
 | Figure 6: ROC curve of actual birth >4kg. Note that 95% confidence level (0.79–0.97), Campbell had the highest sensitivity for estimating macrosomia. With an area under the curve of 0.89, standard error of 0.05 and a sensitivity 81% Campbell had the highest accuracy in predicting macrosomia (birth weight >4.0 kg). ROC – Receiver operating characteristic
Click here to view |
 | Table 4: Operating characteristics of receiver operating characteristic curve depicting, sensitivity, specificity, and accuracy of algorithms in the prediction of normal birth weight
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 | Table 5: Operating characteristics of receiver operating characteristic curve depicting, sensitivity, specificity, and accuracy of algorithms in the prediction of low birth weight
Click here to view |
 | Table 6: Operating characteristics of receiver operating characteristic curve depicting, sensitivity, specificity, and accuracy of algorithms in the prediction of low birth weight area under the operator characteristics curve of macrosomia (actual birth weight >4 kg)
Click here to view |
| Discussion | | |
FW evaluation is an essential aspect of antenatal care. It is used to manage labor, determine delivery routes, treat high-risk pregnancies, and monitor growth. Excessive FW (macrosomia) and intrauterine growth restriction raise the risk of perinatal morbidity and mortality. It is also a predisposing factor to long-term neurologic and developmental anomalies. Accurate FW estimation is needed before deciding when and how to deliver the baby to reduce the risks of complications associated with small and large fetuses. Birth weight is one of the most important indicators of neonatal survival hence its (birth weight) accurate prediction is of utmost importance to obstetricians. Currently, sonography is the most useful adjunct to the clinical method used in assessing overall fetal wellbeing including the prediction of birth weight. Although reliable to a large extent, Dudley reported that “sonographic EFW is associated with a margin of error that ranges from 6% to 11%” and accurate prediction of ABW depends on the population/geographical location where the data were collected from, the fetal part(s) measured and the estimation algorithm used.[37] Per recommendation,[30] we studied the accuracy of the three most common FW estimation algorithms (Hadlock IV, Campbell, and Shepard's algorithm) in Lagos state. We found that the Hadlock IV model is the most accurate FW estimator in our sample.
The mean age observed in this study (28.0 ± 6.6 years) is reflective of the fact that women in their late teens and late 20s who are known to be at “women's peak reproductive years,”[38] were predominant in our sample. Women within the same age bracket as those in our sample were recruited in similar studies.[39],[40],[41],[42],[43] We found that overweight mothers had the highest number of overweight babies [EFW or ABW >4 kg; [Table 2]]. With this result, it could be argued that overweight pregnant women in the population studied [as evidenced by the women's BMI; [Table 2]], has significantly higher chance of having excessive-weight babies. This result thus raises the scepter of genetic linkage between being overweight and having babies that weigh >4 kg in the population studied. Moreover, the statistically significant correlation found between pregnant women's weight and EFW/ABW in our sample reiterates the rather well-known fact that maintaining a healthy weight by women before and during pregnancy improves pregnancy outcome.[43] That birth weight increased significantly (P = 0.003) from the 37th to the 42nd week of pregnancy in our sample has been reported previously.[44] Most women in our sample delivered babies with normal weight, and the fact that the difference between the proportion of women with sonographically predicted normal FW was not significant (P = 0.08) when compared with the proportion of fetuses with normal ABW aligns with results from several previous studies.[1],[27],[34],[45],[46],[47],[48],[49] With these results, we submit (albeit at the moment) that all three algorithms are useful estimators of FW in the cohort studied.
Correlation coefficients are indicators of the strength of the linear relationship between two variables, and a correlation coefficient greater than zero indicates a positive relationship. This study found a statistically significant correlation between EFW and ABW for all three algorithms studied [Table 3]. This result corroborates our earlier submission that “all three algorithms commonly used in the sonographic estimation of FW in Lagos state appear valid.” Similarly, Pearson's coefficient of correlation showed that the Hadlock IV algorithm's EFW had the strongest correlation with ABW. On the other hand, the Campbell algorithm had a stronger correlation than Shepard's model [Table 3]. Similar results[23],[27],[35],[50],[51] as ours have been reported. Therefore, despite the warning that “caution should be exercised when interpreting correlation between two variables since the mere correlation between any two variables does not mean that one causes the other,”[52] this study has shown that the Hadlock IV algorithm has the edge over Campbell's and Shepard's algorithm in predicting FW among Yoruba women.
The ROC curve is a plot of test sensitivity (the y coordinate) versus its 1-specificity or false positive rate (the x coordinate). It is an effective method of evaluating the quality or performance of diagnostic tests and is widely used to evaluate the performance of many radiological tests.[53] A diagnostic model is perfect for detecting positive patients when its sensitivity (also known as the true positive rate) equals 1. However, when that diagnostic modality's (algorithm in this study) sensitivity is 0.5, that diagnostic model would not be considered good enough because such an average sensitivity is merely equivalent to a random occurrence. At 10% confidence interval, the Hadlock IV algorithm was the most accurate predictor of birth weight while the Shepherd model was the least. This is evidenced by 92%, 72%, and 56% accuracy in predicting ABW recorded by Hadlock IV, Campbell, and Shepherd algorithm, respectively. In more specific terms, the Hadlock model was equally the most accurate predictor of normal ABW at the 95% confidence interval as evidenced by its accuracy represented by the [AUC; [Figure 4] and [Table 4]]. The accuracy and sensitivity of the Hadlock IV algorithm in predicting ABW found in this study are similar to the accuracy and sensitivity previously reported.[25],[29],[54],[55],[56] Interestingly, the Hadlock IV algorithm appears more sensitive among fetuses of Yoruba descent than in other populations where the Hadlock equation's accuracy was compared to other algorithms.[34],[45],[55],[57] Regarding low birth weight fetuses, the Hadlock IV also had the highest sensitivity as evidenced by an AUC of 0.94 and a sensitivity of 92% [Figure 5] and [Table 5].
Regarding overweight fetuses, the study found that Campbell's algorithm is the most accurate and most sensitive predictor of macrosomia at 95% confidence interval [Figure 6]. This is evidenced by an AUC of 0.89 and sensitivity of 81% [Table 6]. The fact that the Campbell algorithm was the most accurate predictor of macrosomia (birth weight >4.0 kg) in this study is important when the opinion that “ultrasound tends to underestimate macrosomia in many cases,”[58],[59] is considered. Therefore, despite the Australasian Society for Ultrasound in Medicine's earlier warning that “no formula for determining FW has reached an accuracy that allows us to advocate its use,”[60] the present study has shown that Hadlock IV equation is the most suitable for FW estimation among pregnant women of Yoruba origin residing in Lagos state. This study has shown that reported pitfalls in sonographic EFW could be associated with the algorithm used and the experience and skill of the sonographer.
Limitations of the study
Despite intraobserver error not being statistically significant in this study, we consider the fact that interobserver variability was not estimated as a limitation that might affect the reproducibility of the measurements. Although a midwife measured ABW in our presence in most cases, we only relied on the weight recorded for babies born at the night. We are also convinced that a multi-center study involving a larger sample would give a clearer picture of the validity of all three algorithms as birth weight predictors among Yoruba people.
| Conclusion | | |
While the Hadlock IV and Campbell algorithms commonly used in sonographic prediction of birth weight in Lagos state of Nigeria are valid among fetuses of Yoruba ethnic origin, the Shepherd model is not. Even as the Hadlock formula utilizing sonographically measured BPD, AC, and FL had the lowest random error across the weight classes, the model appears to be the most suitable algorithm whenever low FW is suspected among pregnant women of Yoruba descent who reside in Lagos state.
While the Campbell algorithm is a valid birth weight predictor, the model appears to be the most suitable for women in whom excessive weight fetuses (birth weight >4 kg) are suspected.
The Shepard's model should not be used for FW estimation when low or excessive weight is suspected among pregnant Yoruba women unless an ultrasound machine with either the Hadlock IV or Campbell's algorithm is not available.
Acknowledgment
We would like to thank all the pregnant women who participated in this study for their enthusiasm and cooperation. Their curiosity as evidenced by their desire to understand how sonography is used to estimate fetal weight really energized us.
We also wish to thank our two research assistants (they elected not to be named) that helped us to distribute, explain and retrieve signed informed consent forms.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
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[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6]
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6]
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