Risk prediction of excessive gestational weight gain based on a nomogram model

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=0.453, 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 risk-prediction model developed in this study proved to be effective, providing a valuable basis for early identification and precise intervention in individuals at risk of excessive gestational weight gain.