Hematram Yadav1, Nagarajah Lee. 1. Division of Community Medicine, International Medical University, Kuala Lumpur, Malaysia.
Abstract
AIM: To identify the risk factors influencing the development of macrosomia among pregnant women and to develop a regression model to predict macrosomia. METHODS: A cross-sectional study was conducted in a tertiary hospital in Malaysia involving 2332 pregnant women. The data was retrospectively collected from the obstetrics and gynecology department. The factors that influence fetal weight were collected from the antenatal cards and any additional information was collected by face-to-face interview using a questionnaire. A multiple regression model was developed to predict macrosomia using SPSS ver.18. RESULTS: The significant variables that influence macrosomia in this study were mother's age, mother's body mass index (BMI), weight gain, parity, mother's ethnicity, father's BMI, gestational week, diabetes during pregnancy and neonatal sex. Diabetes during pregnancy is an important risk factor for macrosomia; by using this parameter alone the risk of macrosomia can be predicted with a sensitivity rate of 70% and specificity of 70%. By including other maternal factors such as maternal age, pre-pregnancy BMI, weight gain, parity, ethnicity, as well as father's BMI, gestational weeks and neonate sex, the sensitivity and specificity were improved to 80% and 75%, respectively. CONCLUSION: A regression model was developed and this could be used in health centers to predict macrosomia for purpose of referral to higher centers.
AIM: To identify the risk factors influencing the development of macrosomia among pregnant women and to develop a regression model to predict macrosomia. METHODS: A cross-sectional study was conducted in a tertiary hospital in Malaysia involving 2332 pregnant women. The data was retrospectively collected from the obstetrics and gynecology department. The factors that influence fetal weight were collected from the antenatal cards and any additional information was collected by face-to-face interview using a questionnaire. A multiple regression model was developed to predict macrosomia using SPSS ver.18. RESULTS: The significant variables that influence macrosomia in this study were mother's age, mother's body mass index (BMI), weight gain, parity, mother's ethnicity, father's BMI, gestational week, diabetes during pregnancy and neonatal sex. Diabetes during pregnancy is an important risk factor for macrosomia; by using this parameter alone the risk of macrosomia can be predicted with a sensitivity rate of 70% and specificity of 70%. By including other maternal factors such as maternal age, pre-pregnancy BMI, weight gain, parity, ethnicity, as well as father's BMI, gestational weeks and neonate sex, the sensitivity and specificity were improved to 80% and 75%, respectively. CONCLUSION: A regression model was developed and this could be used in health centers to predict macrosomia for purpose of referral to higher centers.
Authors: Yi Li; Qi-Fei Liu; Dan Zhang; Ying Shen; Kui Ye; Han-Lin Lai; Hai-Qing Wang; Chuan-Lai Hu; Qi-Hong Zhao; Li Li Journal: Clin Nutr Res Date: 2015-04-13
Authors: Thubasni Kunasegaran; Vinod R M T Balasubramaniam; Valliammai Jayanthi Thirunavuk Arasoo; Uma Devi Palanisamy; Amutha Ramadas Journal: Int J Environ Res Public Health Date: 2021-01-31 Impact factor: 3.390
Authors: Hamid Jan Jan Mohamed; See Ling Loy; Amal K Mitra; Satvinder Kaur; Ai Ni Teoh; Siti Hamizah Abd Rahman; Maria Sofia Amarra Journal: BMC Pregnancy Childbirth Date: 2022-04-06 Impact factor: 3.007