Prediction of fetal macrosomia using two-dimensional and three-dimensional ultrasound

Prediction of large-for-gestational age at 36 weeks' gestation: two-dimensional ultrasound vs three-dimensional ultrasound vs magnetic resonance imaging

OBJECTIVE

To compare the performance of two-dimensional ultrasound (2D-US), three-dimensional ultrasound (3D-US) and magnetic resonance imaging (MRI) at 36 weeks' gestation in predicting the delivery of a large-for-gestational-age (LGA) neonate, defined as birth weight ≥ 95th percentile, in patients at high and low risk for macrosomia.

METHODS

This was a secondary analysis of a prospective observational study conducted between January 2017 and February 2019. Women with a singleton pregnancy at 36 weeks' gestation underwent 2D-US, 3D-US and MRI within 15 min for estimation of fetal weight. Weight estimations and birth weight were plotted on a growth curve to obtain percentiles for comparison. Participants were considered high risk if they had at least one of the following risk factors: diabetes mellitus, estimated fetal weight ≥ 90th percentile at the routine third-trimester ultrasound examination, obesity (prepregnancy body mass index ≥ 30 kg/m2) or excessive weight gain during pregnancy. The outcome was the diagnostic performance of each modality in the prediction of birth weight ≥ 95th percentile, expressed as the area under the receiver-operating-characteristics curve (AUC), sensitivity, specificity and positive and negative predictive values.

RESULTS

A total of 965 women were included, of whom 533 (55.23%) were high risk and 432 (44.77%) were low risk. In the low-risk group, the AUCs for birth weight ≥ 95th percentile were 0.982 for MRI, 0.964 for 2D-US and 0.962 for 3D-US; pairwise comparisons were non-significant. In the high-risk group, the AUCs were 0.959 for MRI, 0.909 for 2D-US and 0.894 for 3D-US. A statistically significant difference was noted between MRI and both 2D-US (P = 0.002) and 3D-US (P = 0.002), but not between 2D-US and 3D-US (P = 0.503). In the high-risk group, MRI had the highest sensitivity (65.79%) compared with 2D-US (36.84%, P = 0.002) and 3D-US (21.05%, P < 0.001), whereas 3D-US had the highest specificity (98.99%) compared with 2D-US (96.77%, P = 0.005) and MRI (96.97%, P = 0.004).

CONCLUSIONS

At 36 weeks' gestation, MRI has better performance compared with 2D-US and 3D-US in predicting birth weight ≥ 95th percentile in patients at high risk for macrosomia, whereas the performance of 2D-US and 3D-US is comparable. For low-risk patients, the three modalities perform similarly. © 2023 International Society of Ultrasound in Obstetrics and Gynecology.

Role of fetal head-circumference-to-maternal-height ratio in predicting Cesarean section for labor dystocia: prospective multicenter study

OBJECTIVE

To evaluate the relationship between the fetal head-circumference-to-maternal-height (HC/MH) ratio measured shortly before delivery and the occurrence of Cesarean section (CS) for labor dystocia.

METHODS

This was a multicenter prospective cohort study involving four tertiary maternity hospitals. An unselected cohort of women with a singleton fetus in cephalic presentation, at a gestational age beyond 36 + 0 weeks and without any contraindication for vaginal delivery, was enrolled between September 2020 and November 2021. The MH and fetal HC were measured on admission of the patient to the labor ward. The primary outcome of the study was the performance of the HC/MH ratio in the prediction of CS for labor dystocia. Women who underwent CS for any indication other than failed labor progression, including fetal distress, were excluded from the final analysis.

RESULTS

A total of 783 women were included in the study. Vaginal delivery occurred in 744 (95.0%) women and CS for labor dystocia in 39 (5.0%). CS for labor dystocia was associated with shorter MH (mean ± SD, 160.4 ± 6.6 vs 164.5 ± 6.3 cm; P < 0.001), larger fetal HC (339.6 ± 9.5 vs 330.7 ± 13.0 mm; P < 0.001) and a higher HC/MH ratio (2.12 ± 0.11 vs 2.01 ± 0.10; P < 0.001) compared with vaginal delivery. Multivariate logistic regression analysis showed that the HC/MH ratio was associated independently with CS for labor dystocia (adjusted odds ratio, 2.65 (95% CI, 1.85-3.79); P < 0.001). The HC/MH ratio had an area under the receiver-operating-characteristics curve of 0.77 and an optimal cut-off value for discriminating between vaginal delivery and CS for labor dystocia of 2.09, which was associated with a sensitivity of 0.62 (95% CI, 0.45-0.77), specificity of 0.79 (95% CI, 0.76-0.82), positive predictive value of 0.13 (95% CI, 0.09-0.19) and negative predictive value of 0.98 (95% CI, 0.96-0.99).

CONCLUSIONS

In a large cohort of unselected pregnancies, the HC/MH ratio performed better than did fetal HC and MH alone in identifying those cases that will undergo CS for labor dystocia, albeit with moderate predictive value. The HC/MH ratio could assist in the evaluation of women at risk for CS for labor dystocia. © 2022 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

The sonographic measurement of the ratio between the fetal head circumference and the obstetrical conjugate is accurate in predicting the risk of labor arrest: results from a multicenter prospective study

BACKGROUND

Labor arrest is estimated to account for approximately one-third of all primary cesarean deliveries, and is associated with an increased risk of adverse maternal and perinatal outcomes. One of the main causes is the mismatch between the size of the birth canal and that of the fetus, a condition usually referred to as cephalopelvic disproportion.

OBJECTIVE

This study aimed to describe a new ultrasound predictor of labor arrest leading to cesarean delivery because of suspected cephalopelvic disproportion.

STUDY DESIGN

This was a multicenter prospective study conducted at 3 maternity units from January 2021 to January 2022. A nonconsecutive series of singleton pregnancies with cephalic-presenting fetuses, gestational age of 34 weeks+0 days or above, and no contraindication to vaginal delivery attending at the antenatal clinics of each institution were considered eligible. Between 34+0 and 38+0 weeks of gestation, all eligible patients were submitted to transabdominal 2D ultrasound measurement of the obstetrical conjugate. On admission to the labor ward, the fetal head circumference was measured on the standard transthalamic plane by transabdominal ultrasound. The primary outcome of the study was the accuracy of the ratio between the fetal head circumference and the obstetrical conjugate measurement (ie, head circumference/obstetrical conjugate ratio) in predicting the occurrence of cesarean delivery secondary to labor arrest. The secondary outcome was the relationship between the head circumference/obstetrical conjugate ratio and labor duration.

RESULTS

A total of 263 women were included. Cesarean delivery for labor arrest was performed in 7.6% (20/263) of the included cases and was associated with more frequent use of epidural analgesia (95.0% vs 45.7%; P<.001), longer second stage of labor (193 [120-240] vs 34.0 [13.8-66.5] minutes; P=.002), shorter obstetrical conjugate (111 [108-114] vs 121 [116-125] mm; P<.001), higher head circumference/obstetrical conjugate ratio (3.2 [3.2-3.35] vs 2.9 [2.8-3.0]; P<.001), and higher birthweight (3678 [3501-3916] vs 3352 [3095-3680] g; P=.003) compared with vaginal delivery. At logistic regression analysis, the head circumference/obstetrical conjugate ratio expressed as Z-score was the only parameter independently associated with risk of cesarean delivery for labor arrest (odds ratio, 8.8; 95% confidence interval, 3.6-21.7) and had higher accuracy in predicting cesarean delivery compared with the accuracy of fetal head circumference and obstetrical conjugate alone, with an area under the curve of 0.91 (95% confidence interval, 81.7-99.5; P<.001). A positive correlation between the head circumference/obstetrical conjugate ratio and length of the second stage of labor was found (Pearson coefficient, 0.16; P=.018).

CONCLUSION

Our study, conducted on an unselected low-risk population, demonstrated that the head circumference/obstetrical conjugate ratio is a reliable antenatal predictor of labor arrest leading to cesarean delivery.

Analytical Comparison of Risk Prediction Models for the Onset of Macrosomia Based on Three Statistical Methods

Background and Purpose. Fetal overgrowth can pose a serious threat to the safety of a mother and child. Early identification of high-risk pregnant women and timely pregnancy intervention and guidance are of great value in preventing the development of giant babies and improving adverse maternal and infant outcomes. The current clinical methods for predicting macrosomia mainly rely on obstetric examination and imaging, but their accuracy is controversial. And there is no accepted method for accurately predicting macrosomia. We investigated the risk factors influencing the occurrence of macrosomia and established a prediction model for the occurrence of macrosomia to provide a reference basis for interventions to prevent macrosomia. Method. A retrospective selection of 93 women who were hospitalized in our hospital from March 2019 to May 2022 with a singleton pregnancy and delivered at term with macrosomia were the study group. And 356 women who delivered a normal size baby during the same period were the control group. The variables that were associated with the onset of macrosomia were screened from maternal medical records. Logistic regression models, random forest, and CART decision tree models were developed using the screened variables as input variables and whether they were macrosomia as outcome variables, respectively. The performance of the three models was evaluated by accuracy, precision, recall, F1 score, and receiver operating characteristic curve (ROC). Result. The risk prediction models for the onset of macrosomia, logistic regression model, random forest model, and decision tree, were successfully developed, with accuracies of 0.904, 1.000, and 0.901 in the training set and 0.926, 0.582, and 0.852 in the validation set, respectively. The AUC in the training set were 0.898, 1.000, and 0.789, and in the validation set were 0.906, 0.913, and 0.731, respectively. In general, the logistic regression model has the highest diagnostic efficiency, followed by the random forest model. Conclusion. Logistic regression models have high application value in the assessment of predicting the risk of macrosomia, and it is suggested that the advantages of logistic regression models and random forest models should be combined in future studies and applications to make them work better in the prediction of the risk of macrosomia.

A predictive model of macrosomic birth based upon real-world clinical data from pregnant women

BACKGROUND

Fetal macrosomia is associated with an increased risk of several maternal and newborn complications. Antenatal predication of fetal macrosomia remains challenging. We aimed to develop a nomogram model for the prediction of macrosomia using real-world clinical data to improve the sensitivity and specificity of macrosomia prediction.

METHODS

In the present study, we performed a retrospective, observational study based on 13,403 medical records of pregnant women who delivered singleton infants at a tertiary hospital in Shanghai from 1 January 2018 through 31 December 2019. We split the original dataset into a training set (n = 9382) and a validation set (n = 4021) at a 7:3 ratio to generate and validate our model. The candidate variables, including maternal characteristics, laboratory tests, and sonographic parameters were compared between the two groups. A univariate and multivariate logistic regression was carried out to explore the independent risk factors for macrosomia in pregnant women. Thus, the regression model was adopted to establish a nomogram to predict the risk of macrosomia. Nomogram performance was determined by discrimination and calibration metrics. All the statistical analysis was analyzed using R software.

RESULTS

We compared the differences between the macrosomic and non-macrosomic groups within the training set and found 16 independent risk factors for macrosomia (P < 0.05), including biparietal diameter (BPD), head circumference (HC), femur length (FL), amniotic fluid index (AFI) at the last prenatal examination, pre-pregnancy body mass index (BMI), and triglycerides (TG). Values for the areas under the curve (AUC) for the nomogram model were 0.917 (95% CI, 0.908-0.927) and 0.910 (95% CI, 0.894-0.927) in the training set and validation set, respectively. The internal and external validation of the nomogram demonstrated favorable calibration as well as discriminatory capability of the model.

CONCLUSIONS

Our model has precise discrimination and calibration capabilities, which can help clinical healthcare staff accurately predict macrosomia in pregnant women.

Nomogram-based risk prediction of macrosomia: a case-control study

Background

Macrosomia is closely associated with poor maternal and fetal outcome. But there is short of studies on the risk of macrosomia in early pregnancy. The purpose of this study is to establish a nomogram for predicting macrosomia in the first trimester.

Methods

A case-control study involving 1549 pregnant women was performed. According to the birth weight of newborn, the subjects were divided into macrosomia group and non-macrosomia group. The risk factors for macrosomia in early pregnancy were analyzed by multivariate logistic regression. A nomogram was used to predict the risk of macrosomia.

Results

The prevalence of macrosomia was 6.13% (95/1549) in our hospital. Multivariate logistic regression analysis showed that prepregnancy overweight (OR: 2.13 95% CI: 1.18–3.83)/obesity (OR: 3.54, 95% CI: 1.56–8.04), multiparity (OR:1.88, 95% CI: 1.16–3.04), the history of macrosomia (OR: 36.97, 95% CI: 19.90–68.67), the history of GDM/DM (OR: 2.29, 95% CI: 1.31–3.98), the high levels of HbA1c (OR: 1.76, 95% CI: 1.00–3.10) and TC (OR: 1.36, 95% CI: 1.00–1.84) in the first trimester were the risk factors of macrosomia. The area under ROC (the receiver operating characteristic) curve of the nomogram model was 0.807 (95% CI: 0.755–0.859). The sensitivity and specificity of the model were 0.716 and 0.777, respectively.

Conclusion

The nomogram model provides an effective mothed for clinicians to predict macrosomia in the first trimester.

Development and validation of a new predictive model for macrosomia at late-term pregnancy: A prospective study

OBJECTIVE

Fetal macrosomia is defined as a birth weight more than 4,000 g and is associated with maternal and fetal complications. This early metabolic disease may influence the entire life of the infant. Currently, macrosomia is predicted by using the estimated fetal weight (EFW). However, the EFW is inaccurate when the gestational week is gradually increasing. To assess precisely the risk of macrosomia, we developed a new predictive model to estimate the risk of macrosomia.

METHODS

We continuously collected data on 655 subjects who attended regular antenatal visits and delivered at the Second Hospital of Hebei Medical University (Shijiazhuang, China) from November 2020 to September 2021. A total of 17 maternal features and 2 fetal ultrasonographic features were included at late-term pregnancy. The 655 subjects were divided into a model training set and an internal validation set. Then, 450 pregnant women were recruited from Handan Central Hospital (Handan, China) from November 2021 to March 2022 as the external validation set. The least absolute shrinkage and selection operator method was used to select the most appropriate predictive features and optimize them via 10-fold cross-validation. The multivariate logistical regressions were used to build the predictive model. Receiver operating characteristic (ROC) curves, C-indices, and calibration plots were obtained to assess model discrimination and accuracy. The model's clinical utility was evaluated via decision curve analysis (DCA).

RESULTS

Four predictors were finally included to develop this new model: prepregnancy obesity (prepregnancy body mass index ≥ 30 kg/m²), hypertriglyceridemia, gestational diabetes mellitus, and fetal abdominal circumference. This model afforded moderate predictive power [area under the ROC curve 0.788 (95% confidence interval [CI] 0.736, 0.840) for the training set, 0.819 (95% CI 0.744,0.894) for the internal validation set, and 0.773 (95% CI 0.713,0.833) for the external validation set]. On DCA, the model evidenced a good fit with, and positive net benefits for, both the internal and external validation sets.

CONCLUSIONS

We developed a predictive model for macrosomia and performed external validation in other regions to further prove the discrimination and accuracy of this predictive model. This novel model will aid clinicians in easily identifying those at high risk of macrosomia and assist obstetricians to plan accordingly.

Identification of large-for-gestational age fetuses using antenatal customized fetal growth charts: Can we improve the prediction of abnormal labor course?

INTRODUCTION

Fetal overgrowth is an acknowledged risk factor for abnormal labor course and maternal and perinatal complications. The objective of this study was to evaluate whether the use of antenatal ultrasound-based customized fetal growth charts in fetuses at risk for large-for-gestational age (LGA) allows a better identification of cases undergoing caesarean section due to intrapartum dystocia.

MATERIAL AND METHODS

An observational study involving four Italian tertiary centers was carried out. Women referred to a dedicated antenatal clinic between 35 and 38 weeks due to an increased risk of having an LGA fetus at birth were prospectively selected for the study purpose. The fetal measurements obtained and used for the estimation of the fetal size were biparietal diameter, head circumference, abdominal circumference and femur length, were prospectively collected. LGA fetuses were defined by estimated fetal weight (EFW) >95th centile either using the standard charts implemented by the World Health Organization (WHO) or the customized fetal growth charts previously published by our group. Patients scheduled for elective caesarean section (CS) or for elective induction for suspected fetal macrosomia or submitted to CS or vacuum extraction (VE) purely due to suspected intrapartum distress were excluded. The incidence of CS due to labor dystocia was compared between fetuses with EFW >95th centile according WHO or customized antenatal growth charts.

RESULTS

Overall, 814 women were eligible, however 562 were considered for the data analysis following the evaluation of the exclusion criteria. Vaginal delivery occurred in 466 (82.9 %) women (435 (77.4 %) spontaneous vaginal delivery and 31 (5.5 %) VE) while 96 had CS. The EFW was >95th centile in 194 (34.5 %) fetuses according to WHO growth charts and in 190 (33.8 %) by customized growth charts, respectively. CS due to dystocia occurred in 43 (22.2 %) women with LGA fetuses defined by WHO curves and in 39 (20.5 %) women with LGA defined by customized growth charts (p 0.70). WHO curves showed 57 % sensitivity, 72 % specificity, 24 % PPV and 91 % NPV, while customized curves showed 52 % sensitivity, 73 % specificity, 23 % PPV and 91 % NPV for CS due to labor dystocia.

CONCLUSIONS

The use of antenatal ultrasound-based customized growth charts does not allow a better identification of fetuses at risk of CS due to intrapartum dystocia.

Diagnostic value of sonographic fetal anthropometries and anthropometric formulas to identify macrosomia: a meta-analysis

INTRODUCTION

This study evaluated whether sonographic fetal anthropometries and anthropometric formulas can identify macrosomia, with increases in mortality and morbidity rates in infanthood and probably later in life.

EVIDENCE ACQUISITION

Meta-analysis including good-quality studies determined summarized sensitivity, specificity, and positive and negative likelihood ratios (PLR and NLR, respectively) and area under the curve (AUC). PLR and NLR divided informational usability into exclusion and confirmation strategies (10<PLR and NLR<0.1), confirmation strategies only (10<PLR and NLR>0.1), exclusion strategies only (10>PLR and NLR<0.1), or neither exclusion nor confirmation strategies (10>PLR and NLR>0.1). Subgroup and meta-regression analyses were performed.

EVIDENCE SYNTHESIS

Abdominal circumference showed moderately high sensitivity and moderately high specificity (N.=4). However, informational usability classified it as a neither exclusion nor confirmation strategy. Anthropometric formulas showed high specificity (N.=21). However, use of anthropometric formulas showed low sensitivity, and informational usability classified it as a neither exclusion nor confirmation strategy. On the other hand, limiting to Hadlock IV (1985) formula changed this to a confirmation strategy only (N.=7). Hadlock IV (1985) formula versus other formulas may have been a true confounder.

CONCLUSIONS

Abdominal circumference and varying anthropometric formulas are not highly useful for identification of macrosomia. However, Hadlock IV (1985) formula may be useful for secondary screening of macrosomia.