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Gestational weight management and pregnancy outcomes among women of advanced maternal age

In this study, we assessed the effects of pre-pregnancy body mass index (BMI) and gestational weight gain (GWG) on the pregnancy outcomes of women of advanced age using a back-propagation (BP) artificial neural network. We conducted a retrospective analysis on postpartum and hospital delivery data f...

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Detalles Bibliográficos
Autores principales: Lin, Juan, Fu, Yuelin, Han, Qing, Yan, Jianying, Chen, Rongxin, Zhang, Huale
Formato: Online Artículo Texto
Lenguaje:English
Publicado: D.A. Spandidos 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6676176/
https://www.ncbi.nlm.nih.gov/pubmed/31410130
http://dx.doi.org/10.3892/etm.2019.7752
Descripción
Sumario:In this study, we assessed the effects of pre-pregnancy body mass index (BMI) and gestational weight gain (GWG) on the pregnancy outcomes of women of advanced age using a back-propagation (BP) artificial neural network. We conducted a retrospective analysis on postpartum and hospital delivery data from 1,015 women of advanced maternal age (AMA) hospitalized at the Fujian Provincial Maternity and Children's Hospital from January to June, 2017. Pre-pregnancy overweight was found to increase the incidence of gestational diabetes mellitus (GDM), hypertensive disorders complicating pregnancy (HDCP) and fetal macrosomia. In addition, poor weight gain during pregnancy increased the chances of pre-term births (PTBs). Furthermore, excessive weight gain during pregnancy increased the incidence of macrosomia in women of AMA. On the whole, the findings of this study suggest that controlling the pre-pregnancy BMI and the GWG may reduce the incidence of adverse pregnancy outcomes in women of AMA. The BP neural network is suitable for the study of weight changes in this population.