Establishment of a nomogram model to predict the risk of macrosomia in patients with gestational diabetes mellitus

Abstract

Background

To establish and verify a nomogram model that can predict the risk of macrosomia in patients with gestational diabetes mellitus (GDM).

Methods

Data of patients with GDM who delivered their babies in Shanxi Bethune Hospital between November 2020 and February 2022 were analyzed. Multifactor logistic regression analysis was used to screen the independent risk factors for macrosomia. The model was constructed by R software. The area under the receiver operating characteristic curve (AUC) and goodness-of-fit analysis were used to evaluate its efficiency and accuracy. The clinical application value was evaluated using the decision curve analysis (DCA).

Results

A total of 991 patients with GDM were enrolled for modeling. Multigravida, pre-pregnancy body mass index, family history of hypertension, abdominal circumference, and biparietal diameter were independent risk factors for macrosomia, and the prediction model was established. The AUC in the training and test set were 0.93 (0.89–0.97) and 0.90 (0.84–0.96), respectively, and the difference was not statistically significant. The DCA suggested that the model has a high clinical application value.

Conclusion

The nomogram model for predicting macrosomia in patients with GDM was established. The model has certain accuracy and is expected to be a quantitative tool to guide clinical decision of delivery timing, individualized labor monitoring, and delivery mode.

Keywords:

• Gestational diabetes mellitus
• macrosomia
• nomogram
• prediction model

Ethics approval and consent to participate

This study was approved by the Ethics Committee of Shanxi Bethune Hospital & Shanxi Academy of Medical Sciences, Shanxi Bethune Hospital affiliated to Shanxi Medical University.

Author contributions

Pengyu Sun and Tao Cao, designed the study, collected data and wrote the manuscript. Kang Liu, Xianmei Cui and Liang Zhang supervised the study, provided language help and writing assistance. All authors reviewed and commented on the manuscript. All authors approved the final version of the manuscript.

Availability of data and materials

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This study was supported by the Science and Technology Innovation Program of Shanxi Higher Education Institutions (grant Nos. 2022L161).