Please wait a minute...
Journal of ZheJiang University(Medical Science)  2015, Vol. 44 Issue (3): 264-268    DOI: 10.3785/j.issn.1008-9292.2015.05.05
    
Prediction model of fetal meconium-stained amniotic fluid in re-pregnant women with intrahepatic cholestasis of pregnancy
HE Ling-fei, ZHAO Yun, WANG Zheng-ping
Department of Obstetrics, Women's Hospital, Zhejiang University School of Medicine, Hangzhou 310006, China
Download: HTML (   PDF(830KB)
Export: BibTeX | EndNote (RIS)      

Abstract  

Objective: To establish a prediction model of fetal meconium-stained amniotic fluid in re-pregnant women with intrahepatic cholestasis of pregnancy(ICP). Methods: Clinical data of 180 re-pregnant women with ICP delivering in Women's Hospital, Zhejiang University School of Medicine between January 2009 to August 2014 were collected. An artificial neural network model(ANN) for risk evaluation of fetal meconium-stained fluid was established and assessed. Results: The sensitivity, specificity and accuracy of ANN for predicting fetal meconium-stained fluid were 68.0%, 85.0% and 80.3%, respectively. The risk factors with effect weight >10% were pregnancy complications, serum cholyglycine level,maternal age. Conclusion: The established ANN model can be used for predicting fetal meconium-stained amniotic fluid in re-pregnant women with ICP.



Key wordsNeural networks(computer)      Pregnancy      Fetal monitoring      Cholestasis, intrahepatic      Amniotic fluid      Forecasting     
Received: 17 February 2015      Published: 25 May 2015
CLC:  R714  
Cite this article:

HE Ling-fei, ZHAO Yun, WANG Zheng-ping. Prediction model of fetal meconium-stained amniotic fluid in re-pregnant women with intrahepatic cholestasis of pregnancy. Journal of ZheJiang University(Medical Science), 2015, 44(3): 264-268.

URL:

http://www.zjujournals.com/med/10.3785/j.issn.1008-9292.2015.05.05     OR     http://www.zjujournals.com/med/Y2015/V44/I3/264


应用人工神经网络预测再次妊娠肝内胆汁淤积症孕妇发生羊水浑浊的风险

目的:构建再次妊娠妊娠期肝内胆汁淤积症(ICP)孕妇羊水混浊的预测模型,探讨相关指标的预测价值。方法:收集2009年1月至2014年8月在浙江大学医学院附属妇产科医院再次妊娠住院分娩的ICP孕妇的临床资料及胎儿风险相关资料,应用人工神经网络构建羊水混浊预测模型,分析相关指标对羊水混浊的预测结果和影响权重。结果:应用人工神经网络模型预测ICP孕妇羊水混浊灵敏度为68.0%,特异性为85.0%,准确率为80.3%。参数权重在10%以上的因素有妊娠合并症、孕妇分娩前血清甘胆酸浓度和孕妇年龄。结论:人工神经网络可用于构建ICP孕妇胎儿宫内环境即羊水混浊的预测模型;影响再次妊娠ICP孕妇胎儿宫内安全的危险因素有孕妇年龄、妊娠合并症、孕妇分娩前血清甘胆酸浓度等。


关键词: 神经网络(计算机),  妊娠,  胎儿监测,  胆汁淤积,  肝内,  羊水,  预测 

[1] DIKEN Z, USTA I M, NASSAR A H. A clinical approach to intrahepatic cholestasis of pregnancy[J]. Am J Perinatol, 2014,31(1):1-8.
[2] GEENES V, WILLIAMSON C. Intrahepatic cholestasis of pregnancy[J]. World J Gastroenterol, 2009,15(17):2049-2066.
[3] BACQ Y, SENTIHES L. Intrahepatic cholestasis of pregnancy: diagnosis and management[J]. Clinical Liver Disease, 2014,4(3):58-61.
[4] 中华医学会妇产科学分会产科学组.妊娠期肝内胆汁淤积症诊疗指南[J].中华妇产科杂志, 2011,46(5):391-394. The Subspecialty Group of Obstetrics, Society of Gynecology and Obstetrics, Chinese Medical Association.The guidelines of intrahepatic cholestasis of pregancy[J]. Chinese Journal of Obstetrics and Gynecology, 2011,46(5):391-394.(in Chinese)
[5] BASHEER I A, HAJMEER M. Artificial neural networks: fundamentals, computing, design, and application[J]. J Microbiol Methods, 2000,43(1):3-31.
[6] HAN H G, WANG L D, QIAO J F. Efficient self-organizing multilayer neural network for nonlinear system modeling[J]. Neural Netw, 2013,43:22-32.
[7] MATSUDA Y, KAWAMICHI Y, HAYASHI K, et al. Impact of maternal age on the incidence of obstetrical complications in Japan[J]. J Obstet Gynaecol Res, 2011,37(10):1409-1414.
[8] CAROLAN M, DAVEY M A, BIRO M A, et al. Maternal age, ethnicity and gestational diabetes mellitus[J]. Midwifery, 2012,28(6):778-783.
[9] JAIN R, SURI V, CHOPRA S, et al. Obstetric cholestasis: outcome with active management[J]. J Obstet Gynaecol Res, 2013,39(5):953-959.
[10] WIKSTROM SHEMER E, MARSCHALL H U, LUDVIGSSON J F, et al. Intrahepatic cholestasis of pregnancy and associated adverse pregnancy and fetal outcomes: a 12-year population-based cohort study[J]. BJOG, 2013,120(6):717-723.
[11] SIRISTATIDIS C S, CHRELIAS C, POULIAKIS A, et al. Artificial neural networks in gynaecological diseases: current and potential future applications[J]. Med Sci Monit, 2010,16(10):RA231-236.
[12] CLEOPHAS T J, CLEOPHAS T F. Artificial intelligence for diagnostic purposes: principles, procedures and limitations[J]. Clin Chem Lab Med, 2010,48(2):159-165.
[13] LU J, QI H. Sudden fetal death in a patient with intrahepatic cholestasis of pregnancy complicated with gestational diabetes mellitus[J]. Arch Gynecol Obstet, 2013,287(1):179-182.
[14] MARTINEAU M, RAKER C, POWRIE R, et al. Intrahepatic cholestasis of pregnancy is associated with an increased risk of gestational diabetes[J]. Eur J Obstet Gynecol Reprod Biol, 2014,176: 80-85.

[1] WENG Binghuan,XU Wei,SU Lan,SHEN Min,LI Rong,XU Xiaopeng,LI Lanjuan. Establishment of cell lines for quality control of prenatal genetic diagnosis by SV40LT gene transfection[J]. Journal of ZheJiang University(Medical Science), 2018, 47(5): 520-524.
[2] LI Chen,ZHU Yao,YANG Jinhua,XU Dongsheng,WANG Jianbing,CHEN Kun,LI Qilong. Incidence of lung cancer in Jiashan, Zhejiang province: trend analysis from 1987 to 2016 and projection from 2017 to 2019[J]. Journal of ZheJiang University(Medical Science), 2018, 47(4): 367-373.
[3] LOU Yelin,ZHOU Yimin,LU Hong,LYU Weiguo. Establishment of a prognostic model for preterm delivery in women after cervical conization[J]. Journal of ZheJiang University(Medical Science), 2018, 47(4): 351-356.
[4] JIANG Xiyi,LI Lu,TANG Huijuan,CHEN Tianhui. Multiple risk factors prediction models for high risk population of colorectal cancer[J]. Journal of ZheJiang University(Medical Science), 2018, 47(2): 194-200.
[5] CHEN Yiming, ZHANG Wen, LU sha, MEI Jin, WANG Hao, WANG Shan, GU Linyuan, ZHANG Lidan, CHU Xuelian. Maternal serum alpha fetoprotein and free β-hCG of second trimester for screening of fetal gastroschisis and omphalocele[J]. Journal of ZheJiang University(Medical Science), 2017, 46(3): 268-273.
[6] LI Enshu, YE Xiaoqun, FANG Li, YE Yinghui. Effect of oxygen concentration on outcome of in-vitro fertilization-embryo transfer[J]. Journal of ZheJiang University(Medical Science), 2017, 46(3): 290-294.
[7] SHI Biwei, CUI Long, YE Xiaoqun, YE Yinghui. Effects of embryo cryopreservation and thawing on clinical outcomes of transplantable embryos after cleavage-stage preimplantation genetic diagnosis or screening[J]. Journal of ZheJiang University(Medical Science), 2017, 46(3): 295-299.
[8] TANG Minyue, ZHU Yimin. The involvement of galectin-1 in implantation and pregnancy maintenance at the maternal-fetal interface[J]. Journal of ZheJiang University(Medical Science), 2017, 46(3): 321-327.
[9] SHEN Haiyan, XU Chengfu, CHEN Chunxiao. Platelet count predicts therapeutic response of infliximab for active Crohn's disease[J]. Journal of ZheJiang University(Medical Science), 2016, 45(1): 81-85.
[10] LU Han-ti, LI Fu-dong, LIN Jun-fen, HE Fan, SHEN Yi. Construction of early warning model of influenza-like illness in Zhejiang Province based on support vector machine[J]. Journal of ZheJiang University(Medical Science), 2015, 44(6): 653-658.
[11] SHEN Qin-qin, ZHANG Tan. Research advances on prenatal maternal serum markers for screening adverse pregnancy outcomes[J]. Journal of ZheJiang University(Medical Science), 2015, 44(3): 339-343.
[12] ZHANG Jian-zhen, HE Jing. Risk factors of recurrent preeclampsia and its relation to maternal and offspring outcome[J]. Journal of ZheJiang University(Medical Science), 2015, 44(3): 258-263.
[13] TIAN Ji-shun, PAN Fei-xia, HE Sai-nan, HU Wen-sheng. Risk factors of pregnancy termination at second and third trimester in women with scarred uterus and placenta previa[J]. Journal of ZheJiang University(Medical Science), 2015, 44(3): 247-252.
[14] LIU Yi-feng, YE Xiao-qun, ZHU Lin-ling, HUANG Yun, WU Yi-qing, XU Peng, KONG Yu-jia, LIU Feng, SUN Sai-jun, ZHANG Dan. Factors related to clinical pregnancy outcomes of in vitro fertilization-embryo transfer in women with secondary infertility[J]. Journal of ZheJiang University(Medical Science), 2015, 44(3): 237-246.
[15] LI Miao, ZHU Yi-Min . Impact of hepatitis B virus on sperm parameters and outcome of assisted reproductive technology[J]. Journal of ZheJiang University(Medical Science), 2013, 42(2): 237-241.