Risk prediction of excessive gestational weight gain based on a nomogram model: a prospective observational study in China.

Abstract

Background: Excessive Gestational Weight Gain is a global public health problem with serious and long-term effects on maternal and offspring health. Early identification of at-risk groups and interventions is crucial for controlling weight gain and reducing the prevalence of excessive gestational weight gain. Currently, tools for predicting the risk of excessive gestational weight gain are lacking in China. This study aimed to develop a risk-prediction model and screening tool to identify high-risk groups in the early stages.

Methods: A total of 306 pregnant women were randomly selected who underwent regular obstetric checkups at a tertiary-level hospital in China between January and March 2023. Logistic regression analysis was used to construct the risk-prediction model. The goodness of fit of the model was assessed using the Hosmer-Lemeshow test, and the predictive performance was evaluated using the area under the receiver operating characteristic (ROC) curve, calibration plots, and k-fold cross-validation. R4.3.1 software was used to create a nomogram.

Results: The prevalence of excessive gestational weight gain was 50.32%. Logistic regression analysis revealed that pre-pregnancy overweight (OR = 2.563, 95% CI: 1.043-6.299), obesity (OR = 4.116, 95% CI: 1.396-12.141), eating in front of a screen (OR = 6.230, 95% CI: 2.753 - 14.097); frequency of weekly consumption of sugar-sweetened beverages/desserts/western fast food (OR = 1.948, 95% CI: 1.363-2.785); and pregnancy body image (OR = 1.030, 95% CI: 1.014-1.047) were risk factors for excessive gestational weight gain. Parity (OR = 0.452, 95% CI: 0.275 - 0.740), protective motivation to manage pregnancy body mass (OR = 0.979, 95% CI: 0.958-1), and the time of daily moderate-intensity physical activity (OR = 0.228, 95% CI: 0.113-0.461) were protective factors against excessive gestational weight gain. The area under the ROC curve of the model was 0.885, the mean value of ten-fold cross-validation was 0.857 for AUC.

Conclusion: The nomogram model developed in this study has a good degree of discrimination and calibration, providing a valuable basis for early identification and precise intervention in individuals at risk of excessive gestational weight gain.