Sonographic prediction of macrosomia in pregnancies complicated by maternal diabetes: finding the best formula

Prediction of large-for-gestational-age at birth using fetal biometry in type 1 and type 2 diabetes: A retrospective cohort study

OBJECTIVE

To compare ultrasound-assessed fetal head circumference (HC), abdominal circumference (AC), HC/AC ratio, and estimated fetal weight (EFW) in prediction of large-for-gestational-age (LGA) at birth in pregnancies affected by type 1 (T1DM) and type 2 (T2DM) diabetes.

METHODS

This retrospective cohort study included all women with T1DM and T2DM giving birth to singletons between 2010 and 2019 at Aalborg University Hospital, Denmark. Ultrasound scans were performed at 16, 20, 28 and 34 weeks of pregnancy. LGA was defined as birth weight deviation of 15% or greater from the expected for gestational age (≥90th centile). Prediction of LGA was assessed by logistic regression adjusted for maternal characteristics and glycated hemoglobin (HbA1c) and area under the receiver operating characteristics curve (AUC).

RESULTS

Among 180 T1DM pregnancies, 118 (66%) had an LGA neonate at birth. At 28 weeks of pregnancy, they were predicted with AUCHC/AC = 0.67, AUCAC = 0.85, and AUCEFW = 0.86. The multivariate analysis did not improve the predictive performance of the HC/AC ratio or AC. Among 87 T2DM pregnancies, 36 (41%) had an LGA neonate at birth. At 28 weeks, they were predicted with AUCHC/AC = 0.73, AUCAC = 0.83, and AUCEFW = 0.87. In T2DM, the multivariate analysis significantly improved the predictive performance for both HC/AC ratio and AC from 20 weeks of pregnancy.

CONCLUSION

In T1DM and T2DM pregnancies, LGA is characterized by a general fetal overgrowth including both AC and HC. Therefore, AC and EFW perform better than the HC/AC ratio in the prediction of LGA. In T2DM, as opposed to T1DM, the predictive performance was improved by the inclusion of maternal characteristics and HbA1c in the analysis.

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.

Effective Macrosomia Prediction Using Random Forest Algorithm

(1) Background: Macrosomia is prevalent in China and worldwide. The current method of predicting macrosomia is ultrasonography. We aimed to develop new predictive models for recognizing macrosomia using a random forest model to improve the sensitivity and specificity of macrosomia prediction; (2) Methods: Based on the Shandong Multi-Center Healthcare Big Data Platform, we collected the prenatal examination and delivery data from June 2017 to May 2018 in Jinan, including the macrosomia and normal-weight newborns. We constructed a random forest model and a logistic regression model for predicting macrosomia. We compared the validity and predictive value of these two methods and the traditional method; (3) Results: 405 macrosomia cases and 3855 normal-weight newborns fit the selection criteria and 405 pairs of macrosomia and control cases were brought into the random forest model and logistic regression model. On the basis of the average decrease of the Gini coefficient, the order of influencing factors was: interspinal diameter, transverse outlet, intercristal diameter, sacral external diameter, pre-pregnancy body mass index, age, the number of pregnancies, and the parity. The sensitivity, specificity, and area under curve were 91.7%, 91.7%, and 95.3% for the random forest model, and 56.2%, 82.6%, and 72.0% for logistic regression model, respectively; the sensitivity and specificity were 29.6% and 97.5% for the ultrasound; (4) Conclusions: A random forest model based on the maternal information can be used to predict macrosomia accurately during pregnancy, which provides a scientific basis for developing rapid screening and diagnosis tools for macrosomia.

Diagnostic accuracy of modified Hadlock formula for fetal macrosomia in women with gestational diabetes and pregnancy weight gain above recommended

OBJECTIVES

Women with gestational diabetes (GDM) and weight gain during pregnancy above recommended more often give birth to macrosomic children. The goal of this study was to evaluate the diagnostic accuracy of the modified formula for ultrasound assessment of fetal weight created in a pilot study using a similar specimen in comparison to the Hadlock-2 formula.

METHODS

This is a prospective, cohort, applicative, observational, quantitative, and analytical study, which included 213 pregnant women with a singleton pregnancy, GDM, and pregnancy weight gain above recommended. Participants were consecutively followed in the time period between July 1st, 2016, and August 31st, 2020. Ultrasound estimations were made within three days before the delivery. Fetal weights estimated using both formulas were compared to the newborns' weights.

RESULTS

A total of 133 fetal weight estimations were made. In comparison to the newborns' weight modified formula had significantly smaller deviation in weight estimation compared to the Hadlock-2 formula, higher frequency of deviation within 5% of newborns weights (78.2% [95% CI=0.74-0.83] vs. 60.2%), smaller frequency of deviations from 5 to 10% (19.5 vs. 33.8%) and above 10%, which was even more significant among macrosomic children. There were 36/50 (72%) correctly diagnosed cases of macrosomia by modified and 33/50 (66%) by Hadlock-2 formula. Area under the curve (AUC) for the modified formula was 0.854 (95% CI=0.776-0.932), and for the Hadlock-2 formula 0.824 (95% CI=0.740-0.908). The positive predictive value of the modified formula was 81.81%, the negative 97.91%.

CONCLUSIONS

In cases of greater fetal weights, the modified formula showed greater precision.

Fetal overgrowth in pregnancies complicated by diabetes: validation of a predictive index in an external cohort

PURPOSE

To assess validity of a fetal overgrowth index in an external cohort of women with diabetes in pregnancy METHODS: We performed a retrospective analysis of data derived from women with singleton gestations complicated by diabetes who delivered January 2015-June 2018. The following index variables were used to calculate risk of fetal overgrowth as defined by a customized birthweight ≥ 90th centile: age, history of fetal overgrowth in a prior pregnancy, gestational weight gain, fetal abdominal circumference measurement and fasting glucose between 24 and 30 weeks.

RESULTS

In our validation cohort, 21% of 477 pregnancies were complicated by fetal overgrowth. The predictive index had a bias-corrected bootstrapped area under receiver operating characteristic curve of 0.90 (95% CI 0.86-0.93). 55% of the cohort had a low-risk index (≤ 3) which had a negative predictive value of 97% (95% CI 94-98%), while 18% had a high-risk index (≥ 8) that had a positive predictive value of 74% (95% CI 66-81%).

CONCLUSION

The fetal overgrowth index incorporates five factors that are widely available in daily clinical practice prior to the period of maximum fetal growth velocity in the third trimester. Despite substantial differences between our cohort and the one studied for model development, we found the performance of the index was strong. This finding lends support for the general use of this tool that may aid counseling and allow for targeted allocation of healthcare resources among women with pregnancies complicated by diabetes.

The Role of circRNA-SETD2/miR-519a/PTEN Axis in Fetal Birth Weight through Regulating Trophoblast Proliferation

Abnormal birth weight is the one of the major causes of adulthood diseases such as obesity, metabolic syndrome, cardiovascular disease, type 2 diabetes, and hypertension. Accumulating evidence has suggested that the placental trophoblast is one of the most important reasons that influence birth weight. Our previous study showed that miR-519a are correlated with low fetal birth weight through regulating trophoblast proliferation. To further clarify the detailed mechanisms on how it is regulated, we screened the placental-specific circular RNAs (circRNAs) via microarray assay. The result identified that circ-SETD2 was highly expressed in the placenta of the patients with fetal macrosomia compared with healthy donors. Furthermore, bioinformatic analyses and the luciferase reporter assay revealed that miR-519a possessing the binding sites for both circ-SETD2 and phosphate and tensin homolog was deleted on chromosome 10 (PTEN). Interestingly, upregulation of circ-SETD2 enhanced the proliferation and invasion of the human trophoblast-like cell line HTR8/SVneo cell. A parallel study performed by Western blotting showed that overexpression of circ-SETD2 reduced miR-519a levels and increased PTEN levels in HTR8/SVneo cells. Importantly, the enhancement of HTR8/SVneo cell activity by circ-SETD2 overexpression was nullified when the cells were cotransfected by circ-SETD2 and miR-519a, suggesting the involvement of the circ-SETD2/miR-519a/PTEN axis in trophoblast activity. Taken together, we illustrate the role of circ-SETD2, as an upstream signaling of miR-519a/PTEN, in placenta development via regulating trophoblast proliferation and invasion. These findings improve our understanding of the mechanisms of progression of fetal macrosomia and will guide future development of therapeutic strategies against the disease by targeting the circ-SETD2/miR-519a/PTEN axis.

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.

Ultrasound Fetal Weight Estimation in Diabetic Pregnancies

OBJECTIVES

To evaluate different formulas for estimating fetal weight in diabetic pregnancies.

METHODS

This retrospective study evaluated the precision of ultrasound fetal weight estimation in 756 pregnancies complicated by gestational diabetes between 2002 and 2016. The estimated fetal weights (EFWs) were obtained within 7 days of delivery from 10 weight estimation formulas and were compared with pair-wise matched controls from 15,701 patients. The precision of the evaluated formulas for EFW was analyzed by median absolute percentage errors (MAPEs), mean percentage errors (MPEs), and proportions of estimates within 10% of actual birth weight.

RESULTS

Among the tested formulas, the lowest MAPE was detected with formula I of Hadlock et al (Am J Obstet Gynecol 1985; 151:333-337), and the formula of Schild et al (Ultrasound Obstet Gynecol 2004; 23:30-35) had the highest proportion of estimates within the 10% range. The EFW in diabetic patients showed a slight trend toward overestimation in comparison with the matched controls (MPE estimates showed a trend toward more positive values). In most of the EFW formulas that were evaluated, no significant differences were detected in MAPEs and estimates within the 10% range. The MPE estimates with most formulas in both groups were close to zero. Overall, the differences between most of the evaluated formulas were small.

CONCLUSIONS

Little evidence was found for differences in the accuracy of the EFW in diabetic pregnancies and controls. The Hadlock I formula showed the lowest MAPE, and the Schild formula had the highest proportion of estimates within the 10% range.