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

Abstract

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.

Keywords: fetal growth; gestational diabetes mellitus; macrosomia; obesity; predictive model.

Figure 1

Study flow chart.

Figure 2

(A) Feature selection via the least absolute shrinkage and selection operator (LASSO) regression model. (A) The LASSO coefficient profiles of 19 features. The coefficient profile plot was conducted against the log (lambda, λ) sequence. The dotted vertical line was drawn at the lambda with a minimum mean squared error (lambda.min); nine features were selected by the LASSO regression. The solid vertical line was drawn at the lambda.min with one standard error (lambda.1se); four features were selected by the LASSO regression model. (B) Feature selection via 10-fold cross-validation. (B) The optimal parameter (lambda, λ) selection in the Lasso regression model used 10-fold cross-validation via the minimum criteria. The partial likelihood deviance (binomial deviance) curve was plotted versus logλ. The dotted vertical line was drawn at the lambda with a minimum mean squared error (lambda.min); nine features were selected. The solid vertical line was drawn at the lambda.min with one standard error (lambda.1se); four features were selected.

Figure 3

A nomogram prediction model of macrosomia. Four predictors were included: the prepregnancy obesity, hypertriglyceridemia, GDM, and fetal abdominal circumference. The score of each predictor were determined from each feature axis to the total points axis by following the vertical line. GDM, gestational diabetes mellitus; fetal AC, fetal abnormal circumference.

Figure 4

Receiver operating characteristic curves of macrosomia risk nomogram prediction. Receiver operating characteristic curve (ROC) of the (A) training set, (B) internal validation set, (C) external validation set. AUC, area under the receiver operating characteristic curve.

Figure 5

Calibration plots of macrosomia risk nomogram prediction. The x-axis represents the predicted risk of macrosomia. The y-axis represents the actual diagnosed case of macrosomia. The diagonal dotted line represents a perfect prediction by an ideal model. The solid line represents the performance of the (A) training set, (B) internal validation set, (C) external validation set. The closer fit of solid line to the diagonal dotted line represents a better prediction.

Figure 6

Decision curve analysis of macrosomia risk nomogram prediction. DCA of the (A) training set, (B) internal validation set, and (C) external validation set. The x-axis measures the threshold probability. The y-axis measures the net benefit. The thick, black solid line represents the macrosomia risk nomogram. The thin, black horizontal line (none line) represents the assumption that no patients are non-adherent to medication, which means that the net benefit is zero. The thin, gray bias (all line) represents the assumption that all patients are non-adherent to medication. DCA, decision curve analysis.