1. A predictive model of macrosomic birth based upon real-world clinical data from pregnant women
- Author
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Gao Jing, Shi Huwei, Chen Chao, Chen Lei, Wang Ping, Xiao Zhongzhou, Yang Sen, Chen Jiayuan, Chen Ruiyao, Lu Lu, Luo Shuqing, Yang Kaixiang, Xu Jie, and Cheng Weiwei
- Subjects
China ,Pregnancy ,Risk Factors ,Infant, Newborn ,Parturition ,Humans ,Obstetrics and Gynecology ,Female ,Pregnant Women ,Weight Gain ,Fetal Macrosomia ,Retrospective Studies - Abstract
Background Fetal macrosomia is associated with an increased risk of several maternal and newborn complications. Antenatal predication of fetal macrosomia remains challenging. We aimed to develop a nomogram model for the prediction of macrosomia using real-world clinical data to improve the sensitivity and specificity of macrosomia prediction. Methods In the present study, we performed a retrospective, observational study based on 13,403 medical records of pregnant women who delivered singleton infants at a tertiary hospital in Shanghai from 1 January 2018 through 31 December 2019. We split the original dataset into a training set (n = 9382) and a validation set (n = 4021) at a 7:3 ratio to generate and validate our model. The candidate variables, including maternal characteristics, laboratory tests, and sonographic parameters were compared between the two groups. A univariate and multivariate logistic regression was carried out to explore the independent risk factors for macrosomia in pregnant women. Thus, the regression model was adopted to establish a nomogram to predict the risk of macrosomia. Nomogram performance was determined by discrimination and calibration metrics. All the statistical analysis was analyzed using R software. Results We compared the differences between the macrosomic and non-macrosomic groups within the training set and found 16 independent risk factors for macrosomia (P Conclusions Our model has precise discrimination and calibration capabilities, which can help clinical healthcare staff accurately predict macrosomia in pregnant women.
- Published
- 2022