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

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 incidence 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=0.453, 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.

背景：妊娠期过度体重增长（Excessive Gestational Weight Gain）是一项全球性公共卫生问题，对孕产妇及子代健康存在严重且持久的不良影响。早期识别高危人群并实施干预，对于控制孕期体重增长、降低妊娠期过度体重增长的发生率至关重要。目前，我国尚缺乏针对妊娠期过度体重增长的风险预测工具。本研究旨在构建风险预测模型与筛查工具，以实现早期高危人群的识别。
方法：2023年1月至3月，于国内某三级医院定期接受产科产检的孕妇中，随机选取306名作为研究对象。采用Logistic回归分析构建妊娠期过度体重增长风险预测模型。通过Hosmer-Lemeshow拟合优度检验评估模型拟合度，采用受试者工作特征（Receiver Operating Characteristic, ROC）曲线下面积、校准曲线及k折交叉验证对模型预测性能进行评价。使用R4.3.1软件构建列线图（nomogram）。
结果：本研究中妊娠期过度体重增长的患病率为50.32%。Logistic回归分析结果显示，孕前超重（OR=2.563，95%CI:1.043~6.299）、孕前肥胖（OR=4.116，95%CI:1.396~12.141）、屏幕前进食习惯（OR=6.230，95%CI:2.753~14.097）、每周食用含糖饮料/甜点/西式快餐的频率（OR=1.948，95%CI:1.363~2.785）以及孕期身体意象（OR=1.030，95%CI:1.014~1.047）均为妊娠期过度体重增长的危险因素。而孕次（OR=0.453，95%CI:0.275~0.740）、孕期体重管理保护动机（OR=0.979，95%CI:0.958~1）以及每日中等强度体力活动时长（OR=0.228，95%CI:0.113~0.461）则为该病的保护因素。本模型的ROC曲线下面积为0.885，十折交叉验证的平均AUC值为0.857。
结论：本研究构建的风险预测模型具有良好的预测效能，可为妊娠期过度体重增长高危人群的早期识别与精准干预提供重要依据。