小儿胎龄儿危险因素及其预测模型的研究

发布时间：2018-06-29 15:30

  本文选题：小于胎龄儿 + 危险因素　； 参考：《北京协和医学院》2015年博士论文

【摘要】：研究目的：小于胎龄儿(small for gestational age, SGA)是对出生体重小于一定标准的一类新生儿的总称,这部分新生儿不但在围产期有着极高的风险,其在学龄期以及成年期也会存在着一系列问题。具体而言,SGA患儿在围产期的死亡率较体重正常的新生儿高出很多,而且其罹患新生儿低血糖、新生儿低体温、新生儿呼吸窘迫综合征(neonatal respiratory distress Syndrome, NRDS)、Ⅲ级以上的早产儿视网膜病(retinopathy of prematurity, ROP)以及坏死性小肠结肠炎(necrotizing enterocolitis, NEC)等疾病的风险也相应增加；同时,当这部分孩子成长至学龄期,亦容易出现认知障碍、学习能力下降的情况；而在成年之后,他们之中将有很多的人的成年终身高低于人群的-2标准差(standard deviation, SD);此外,近年来,有回顾性研究发现,出生时为SGA的人其成年后出现超重或肥胖、糖代谢和脂代谢异常甚至是高血压的可能性亦明显升高。正是因为SGA患儿在人生中的各个时期都存在着一系列的健康问题,故一直以来,研究者们一直在寻找造成SGA的危险因素方面努力探寻,并期待能够通过建立SGA风险预测模型来实现对SGA的早期诊断,从而为早期干预SGA打下基础。截至目前为止,很多研究都发现,遗传因素,母亲的基本情况以及孕前和孕期生活情况、心理状态和健康情况等因素均可影响新生儿体重。但这些研究所着眼的项目多仅集中在饮食、药物和疾病等有限的方面,样本量较小,而且多为回顾性或观察性的研究,其研究结论可靠性及实用性亦有待商榷。同时,近年来研究者们所建立出的预测模型多需要进行很多正常产检之外的检查项目,而且有些项目还与医师经验水平密切相关,这使得上述模型在临床实际操作过程中存在着很大的局限性。因而,我们拟就SGA发生的危险因素的问题进行一项覆盖面、样本量大的研究,并在此基础上依据支持向量机(support vector machine, SVM)理论建立SGA的预测模型,以期为日后对SGA采取早期干预措施打下基础。研究方法：我们采用2010年至2012年“国家免费孕前优生健康检查项目”中的相关数据,经过变量筛选、数据预处理等工作,采用卡方检验、Mann-WhitneyU检验等方法对数据进行单因素分析,找到与新生儿发生SGA相关的变量,并进一步采用二分类Logistics回归分析的方法对与新生儿发生SGA有相关关系的变量进行多因素分析。最后依据SVM理论建立SGA风险预测模型。结果：1.在研究人群中,如以出生体重低于同胎龄同性别的新生儿的出生体重的第3百分位数为诊断标准时,SGA发生率为6.0%,其中男婴的发生率为6.3%,而女婴的为5.6%；若以出生体重低于同胎龄同性别的新生儿的出生体重的第10百分位数为诊断标准时,SGA的发生率为11.2%,男婴的发生率为12.0%,女婴的为10.4%。从地域分布来看,在以第3和第10百分位数为诊断标准时,北方人口的SGA发生率分别为5.1%和9.4%,而这一数字在南方人口中达到了6.4%和12.0%。2.父母亲人口学特征、社会心理状态、生活习惯、既往生殖系统健康情况、环境有害因素暴露情况、母亲孕期状态以及其他理化检查结果等变量与孩子会对孩子是否发生SGA产生一定的影响。3.通过SVM理论可建立的新生儿SGA风险预测模型,在以第3和第10百分位数为诊断标准时,其预测准确度分别为：99.227%和83.239%。结论：父母亲人口学特征、社会心理状态、生活习惯、既往生殖系统健康情况、环境有害因素暴露情况、母亲孕期状态以及其他理化检查结果等变量与孩子会对孩子是否发生SGA产生一定的影响。此外,通过SVM理论可建立新生儿SGA风险预测模型,且预测效能较高。
[Abstract]:Research objectives: small for gestational age (SGA) is the general term for a class of newborns with a certain birth weight less than a certain standard. This part of the newborn not only has a high risk of perinatal period, but also has a series of problems at the school age and in adulthood. Specifically, the mortality of children in the perinatal period is more than that of SGA. Normal newborn babies are much higher, and they suffer from neonatal hypoglycemia, hypothermia of the newborn, neonatal respiratory distress syndrome (neonatal respiratory distress Syndrome, NRDS), retinopathy of retinopathy of prematurity (ROP) above grade III and necrotizing enterocolitis (necrotizing enterocolitis, NEC), etc. The risk of disease increases correspondingly; at the same time, when this part of the child grows to the school age, it is also prone to cognitive impairment and a decline in learning ability; and in adulthood, many of them have a year-end height below the -2 standard (standard deviation, SD). In addition, in recent years, a retrospective study found that People who were born SGA were overweight or obese in adulthood, and the possibility of glucose metabolism and lipid metabolism, even hypertension, was also significantly higher. It is because children in SGA have a series of health problems at all times of life, so researchers have been searching for the risk factors for SGA. It is expected that the early diagnosis of SGA can be realized through the establishment of the SGA risk prediction model, thus laying the foundation for early intervention in SGA. So far, many studies have found that genetic factors, the basic situation of mothers, life conditions in pregnancy and pregnancy, mental state and health condition can affect the weight of the newborn. Most of the projects focused on limited areas such as diet, medicine and disease, small sample size, and many retrospective or observational studies, the reliability and practicability of its findings are still open to discussion. And some projects are also closely related to the level of the physician's experience, which makes the above model very limited in clinical practice. Therefore, we have a coverage of the risk factors for SGA, a large sample of samples, and based on the support vector machine (support vector machine, SVM). The theory set up the prediction model of SGA in order to lay the foundation for the early intervention measures for SGA. Research methods: we adopt the relevant data in the "national free prenatal health examination project" from 2010 to 2012, through the selection of variables, data preprocessing, and the use of chi square test, Mann-WhitneyU test and other methods. According to the single factor analysis, we found the variables related to the occurrence of SGA in the newborn, and further used the two classification Logistics regression analysis to analyze the variables related to the newborn SGA. Finally, the SGA risk prediction model was established based on the SVM theory. Results: 1. in the study population, such as the birth weight is lower than the birth weight. The incidence of SGA was 6% in the third percentile of birth weight at the same age of the same gestational age. The incidence of male babies was 6.3%, while the female baby was 5.6%. The incidence of SGA was 11.2% when the birth weight was lower than the same birth weight of the same baby. The incidence of the baby was 11.2%. The incidence of 10.4%. was 12%. From the geographical distribution, the incidence of SGA in the northern population was 5.1% and 9.4%, respectively, with the third and tenth percentiles as the diagnostic criteria, and this number reached 6.4% and 12.0%.2. in the southern population. The exposure of environmental harmful factors, maternal pregnancy state and other physical and chemical tests and other variables and children will have a certain influence on the child's occurrence of SGA, and the.3. model of neonatal SGA risk can be established through the SVM theory. The prediction accuracy is 99.227% and 83., respectively, with the third and tenth percentiles as the diagnostic criteria. 239%. conclusion: the oral and family characteristics, social psychological state, life habits, the health of the reproductive system, the exposure of the environmental harmful factors, the mother's pregnancy state and other physical and chemical examination results have a certain effect on the child's occurrence of SGA. In addition, the risk of neonatal SGA can be established through the SVM theory. Prediction model and high prediction efficiency.
【学位授予单位】：北京协和医学院
【学位级别】：博士
【学位授予年份】：2015
【分类号】：R722.1
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本文编号：2082562