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A predictive model of macrosomic birth based upon real-world clinical data from pregnant women
- Source :
- BMC Pregnancy and Childbirth, Vol 22, Iss 1, Pp 1-10 (2022)
- Publication Year :
- 2022
- Publisher :
- BMC, 2022.
-
Abstract
- 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
- Subjects :
- Macrosomia
Prediction model
Nomogram
Clinical data
Gynecology and obstetrics
RG1-991
Subjects
Details
- Language :
- English
- ISSN :
- 14712393
- Volume :
- 22
- Issue :
- 1
- Database :
- Directory of Open Access Journals
- Journal :
- BMC Pregnancy and Childbirth
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.16cc574badce483587aa6fdd8ebbdb33
- Document Type :
- article
- Full Text :
- https://doi.org/10.1186/s12884-022-04981-9