Preliminary study on the risk of macrosomia using Bayesian discriminant analysis based on prenatal records

WU Liu-yu; LAN Jing-you; HUANG Dan-dan; QIU Xiao-qiang; LIU Mei-liang; LIANG Qiu-li; ZHANG Di; ZENG Xiao-yun

doi:10.16462/j.cnki.zhjbkz.2019.11.008

Volume 23 Issue 11

Nov. 2019

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Article Navigation > CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION > 2019 > 23(11): 1338-1341, 1347

WU Liu-yu, LAN Jing-you, HUANG Dan-dan, QIU Xiao-qiang, LIU Mei-liang, LIANG Qiu-li, ZHANG Di, ZENG Xiao-yun. Preliminary study on the risk of macrosomia using Bayesian discriminant analysis based on prenatal records[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2019, 23(11): 1338-1341, 1347. doi: 10.16462/j.cnki.zhjbkz.2019.11.008

Citation:

WU Liu-yu, LAN Jing-you, HUANG Dan-dan, QIU Xiao-qiang, LIU Mei-liang, LIANG Qiu-li, ZHANG Di, ZENG Xiao-yun. Preliminary study on the risk of macrosomia using Bayesian discriminant analysis based on prenatal records[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2019, 23(11): 1338-1341, 1347. doi: 10.16462/j.cnki.zhjbkz.2019.11.008

Citation:

WU Liu-yu, LAN Jing-you, HUANG Dan-dan, QIU Xiao-qiang, LIU Mei-liang, LIANG Qiu-li, ZHANG Di, ZENG Xiao-yun. Preliminary study on the risk of macrosomia using Bayesian discriminant analysis based on prenatal records[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2019, 23(11): 1338-1341, 1347. doi: 10.16462/j.cnki.zhjbkz.2019.11.008

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Preliminary study on the risk of macrosomia using Bayesian discriminant analysis based on prenatal records

doi: 10.16462/j.cnki.zhjbkz.2019.11.008

1.
Department of Epidemiology and Health Statistics, School of Public Health, Guangxi Medical University, Nanning 530021, China
2.
Department of obstetrics, Wuming Hospital of Guangxi Medical University, Nanning 530199, China
3.
Department of obstetrics, Wuming District Maternity and Child Care Hospital, Nanning 530199, China

Funds:

National Nature Science Foundation of China 81460517

More Information

Corresponding author: ZENG Xiao-yun, E-mail: zxyxjw@21cn.com
Received Date: 2019-03-28
Rev Recd Date: 2019-07-14
Publish Date: 2019-11-10

Abstract

Abstract

Objective To explore the clinical effect of Bayesian discriminant analysis in predicting the risk of macrosomia. Methods 169 fetal macrosomia and 169 non-macrosomia were enrolled in a 1:1 matched case-control study. Conditional Logistic regression was used to select the discriminant indexes, and the discriminant indexes were put into the Bayesian discriminant model to obtain the Bayesian discriminant function. The discriminant function was the retrospectively examined and externally tested. Results The results of conditional Logistic regression model indicated that mother's height, early pregnancy body mass index (BMI), gestational diabetes, gestational weeks, the height of uterine and abdominal circumference were associated with the birth of fetal macrosomia. The Bayesian discriminant function were established: Fetal macrosomia: y1=-27.802+8.420×Mother's height+8.719×early pregnancy BMI+10.485×gestational weeks+3.375×gestational diabetes+2.862×height of uterine and abdominal circumference; Non-macrosomia y2=-17.477+7.161×Mother's height+7.217×early pregnancy BMI+7.862×gestational weeks+2.036×gestational diabetes-0.085×height of uterine and abdominal circumference. Wilks' Lambda λ=0.489, P < 0.001, the Bayesian discriminant function was statistically significant. The internal and external conformity rates of the Bayesian discriminant model were all more than 80%. Conclutions The birth of fetal macrosomia is related to many factors. The Bayesian discriminant model in the present study is valuable to discriminate macrosomia and provide an objective reference for more accurate identification of macrosomia in the future.
- Macrosomia,
- Influencing factors,
- Bayesian discriminant

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References(21)

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