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

Mazzone, Eleonora; Dall’Asta, A.; Kiener, A.; Carpano, Maria Giovanna; Suprani, Alice; Ghi, T.; Frusca, T.

doi:10.1016/j.ejogrb.2019.10.003

Cited by 14 publications

(13 citation statements)

References 28 publications

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“…The most objective method currently employed to estimate fetal body weight is ultrasonographic (US) measurement which encompasses over 30 different formulas for the US estimates to predict newborn birth weight [16][17][18] , with the most widely used being the Hadlock formula 19 . To generate sonographic fetal weight estimations with a lower error margin, many formulas have re ected disparate parameters of the fetus (fetal abdominal fat layer 20 , shoulder soft-tissue thickness 21 , biacromial diameter 22 ), and some have even entailed 3D sonographic measurements 23 . Although such formulas and novel predictors may improve the accuracy of US evaluation, they nevertheless increase technical di culty and sonication time.…”

Section: Discussionmentioning

confidence: 99%

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

Gao

Shi

Chen

et al. 2022

Preprint

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Aim: To develop a nomogram model for the prediction of macrosomia using real-world clinical data.Methods: In the present study, we retrospectively analyzed the medical records of pregnant women who delivered singleton infants at a tertiary hospital in Shanghai from 1 January 2018 through 31 December 2019. We extracted the data from a total of 13,403 pregnant women for this study, with the original dataset split into a training set (n = 9,382) and a validation set (n = 4,021) at a 7:3 ratio to generate and validate our model. The independent risk factors for macrosomia in pregnant women were analyzed employing multivariate logistic regression, and the nomogram model used to predict the risk of macrosomia in pregnant women was established and verified with 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.

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Section: Discussionmentioning

confidence: 99%

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

Gao

Shi

Chen

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The most objective method currently employed to estimate fetal body weight is ultrasonographic (US) measurement, which encompasses over 30 different formulas for the US estimates to predict newborn birth weight [16][17][18], with the most widely used being the Hadlock formula [19]. To generate sonographic fetal weight estimations with a lower error margin, many formulas have reflected disparate parameters of the fetus (fetal abdominal fat layer [20], shoulder soft-tissue thickness [21], biacromial diameter [22]), and some have even entailed 3D sonographic measurements [23]. Although such formulas and novel predictors may improve the accuracy of US evaluation, they nevertheless increase technical difficulty and sonication time.…”

Section: Discussionmentioning

confidence: 99%

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

Gao

Shi

Chen

et al. 2022

BMC Pregnancy Childbirth

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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.

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“…Massive infants increase the rate of cesarean delivery and perinatal complications and have been recognized as a major cause of maternal and neonatal mortality. Currently, in clinical practice, prediction of macrosomia mainly relies on imaging measurements and obstetric examination to measure uterine height, but the accuracy of these two methods is still controversial [ 7 – 9 , 13 , 14 ]. In the study of constructing statistical models for predicting macrosomia, imaging measurement indicators and traditional data analysis methods are still dominant [ 15 , 16 ].…”

Section: Discussionmentioning

confidence: 99%

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

Zhang

Song

2022

Disease Markers

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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.

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

Cited by 14 publications

References 28 publications

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

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

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

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

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