Analysis of Factors Affecting Abnormal Gestational Weight Gain and Construction of Risk Prediction Models

• Published in: Clinical and experimental obstetrics & gynecology 2024-09, Vol.51 (9), p.198
• Main Authors: Chen, Xiaoqin, Lan, Liubing, Zhong, Qiuping, He, Yanhong, Zeng, Mei, Hu, Yonghe, Lai, Fengdan
• Format: Article
• Language: English
• Subjects: body weight; gestational weight gain; influencing factors; nomogram; pregnancy
• ISSN: 0390-6663; 2709-0094
• DOI: 10.31083/j.ceog5109198

Background: Herein, we aimed to investigate the factors influencing abnormal gestational weight gain (GWG) during pregnancy and to develop a risk model for predicting deviations in GWG among pregnant women. Methods: A retrospective analysis was conducted on the clinical data of 1200 pregnant women from May 2018 to May 2020, according to the standards recommended by the American Academy of Medicine in 2009. The pregnant women were divided into three groups: 186 cases in the weight gain below the recommended GWG (low GWG) group, 433 cases in the normal GWG group, and 581 cases in the weight gain above the recommended GWG (high GWG) group. Additionally, clinical data of 515 pregnant women who established perinatal records at our hospital and underwent regular antenatal examinations and deliveries from May 2020 to May 2022 were collected to serve as the validation group for external verification of the model. Single-factor and multi-factor logistic regression analyses were conducted to identify the factors influencing weight gain below or above the recommended GWG in pregnant women and to construct a risk model for predicting deviations in weight gain. The calibration curves and receiver operating characteristic (ROC) curves were plotted, and the area under the curve (AUC) was calculated to evaluate the performance of the risk prediction model. Results: Being underweight before pregnancy was identified as an independent risk factor for low GWG (p < 0.05), while primiparity and pregnancy occurring in spring and summer were found to be protective factors (p < 0.05). Obesity before pregnancy, a history of fetal macrosomia, and pregnancy occurring in spring and summer were identified as independent risk factors for high GWG (p < 0.05), whereas regular exercise during pregnancy was a protective factor (p < 0.05). The slope of the calibration curve for predicting weight gain deviations closely approached 1, with Hosmer-Lemeshow goodness-of-fit test values of Chi-square (χ2) = 8.388, 7.295, p = 0.397, 0.505; and AUCs of 0.753 and 0.761, respectively. External validation results indicated that the predicted probabilities closely matched the actual probabilities, demonstrating good consistency, with AUCs of 0.747 and 0.877, respectively. Conclusions: The risk prediction model constructed in this study, incorporating pre-pregnancy body mass index (BMI) and the season of pregnancy, plays a crucial role in individually predicting weight gain deviations during pregnancy. T