A predictive model of macrosomic birth based upon real-world clinical data from pregnant women.

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 character

SUBMITTER: Jing G 

PROVIDER: S-EPMC9386989 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature