發(fā)布時間：2018-03-23 23:08

  本文選題：胎兒體重　切入點：回歸方程　出處：《華北理工大學(xué)》2017年碩士論文

【摘要】：目的探討基于超聲檢查預(yù)測足月胎兒出生體重的最佳模型;分析尋找出現(xiàn)巨大兒時臨床參數(shù)、超聲參數(shù)的臨界參考值;分析尋找出現(xiàn)巨大兒的危險因素。方法選取2015年10月至2017年01月在華北理工大學(xué)附屬醫(yī)院產(chǎn)科住院分娩的單胎孕足月孕婦407例,均在產(chǎn)前0-5天進(jìn)行胎兒超聲檢查。將407例胎兒分為:A組(非巨大兒組FW4000g),337例;B組(巨大兒組FW≥4000g),70例。超聲測量參數(shù)包括:雙頂徑-BPD、枕額徑-OFD、頭圍-HC、小腦橫徑-TCD、肝臟長度-LL、腹橫徑、腹前后徑、腹圍-AC、股骨長度-FL及股骨中段皮下軟組織厚度-FSTT等。收集的臨床參數(shù)包括:孕婦身高、體重、孕期增重,孕周、宮高、腹圍,生化指標(biāo)等。采用Excel 2013建立數(shù)據(jù)庫,SPSS 20.0進(jìn)行統(tǒng)計學(xué)分析,正態(tài)分布的資料以((?)±s)表示,偏態(tài)資料以中位數(shù)(四分位數(shù)間距)表示。各參數(shù)與胎兒體重的相關(guān)性分析采用Pearson相關(guān)性分析;建立新的預(yù)測胎兒體重回歸方程采用多重線性回歸分析法;計量資料間的比較采用單因素方差分析、t檢驗、秩和檢驗,組內(nèi)兩兩之間比較采用LSD檢驗,計數(shù)資料間的比較采用卡方檢驗;各參數(shù)預(yù)測巨大兒臨界參考值的確定采用ROC曲線分析;分析巨大兒的危險因素采用Logistic回歸分析;以P0.05差異有統(tǒng)計學(xué)意義。結(jié)果1已有的19種回歸方程預(yù)測胎兒體重準(zhǔn)確性的比較:(1)在5種臨床參數(shù)方程中,非巨大兒組及整體組采用卓晶如法、巨大兒組采用羅來敏法預(yù)測胎兒體重時絕對誤差、相對誤差均小于其余4個方程(P0.05),表明其預(yù)測胎兒體重準(zhǔn)確性高于其余4個方程;(2)在14種臨床參數(shù)方程中,非巨大兒組、整體組采用Hadlock FP(BPD、HC、AC、FL)、巨大兒組采用Merz E(BPD、AC)預(yù)測胎兒體重時絕對誤差、相對誤差均小于其余13個方程(P0.05),表明其預(yù)測胎兒體重準(zhǔn)確性高于其余13種方程;2建立新的預(yù)測胎兒體重回歸方程:(1)各參數(shù)與胎兒體重的相關(guān)性分析:AC(r=0.806,P0.05)與胎兒體重的相關(guān)程度最密切;(2)新建立三個回歸方程:(1)臨床參數(shù)方程N(yùn)ew Equation 1;(2)超聲參數(shù)方程:New Equation 2;(3)聯(lián)合參數(shù)方程N(yùn)ew Equation 3。(3)在新建立的3種方程中,不同組別采用New Equation 3預(yù)測胎兒體重時絕對誤差、相對誤差均小于其余2個方程(P0.05),表明其預(yù)測胎兒體重的準(zhǔn)確性高于其余2個新方程;3在所有22種方程中(現(xiàn)有19種方程及新建立的3種方程),不同組別采用New Equation 3預(yù)測胎兒體重時絕對誤差、相對誤差均小于其余21個方程(P0.05),表明New Equation 3為預(yù)測胎兒體重準(zhǔn)確性最高的回歸方程。4 BP人工神經(jīng)網(wǎng)絡(luò)模型預(yù)測胎兒體重:(1)在訓(xùn)練樣本數(shù)一定范圍內(nèi),超聲參數(shù)、聯(lián)合參數(shù)的BP人工神經(jīng)網(wǎng)絡(luò)模型預(yù)測胎兒體重絕對誤差、相對誤差,隨著訓(xùn)練樣本數(shù)的增加而降低(P0.05),表明提高超聲參數(shù)、聯(lián)合參數(shù)的BP人工神經(jīng)網(wǎng)絡(luò)模型訓(xùn)練樣本數(shù)可以提高預(yù)測胎兒體重準(zhǔn)確性。(2)在各組別中,超聲參數(shù)、聯(lián)合參數(shù)的BP人工神經(jīng)網(wǎng)絡(luò)模型預(yù)測胎兒體重的絕對誤差、相對誤差均小于回歸方程法(P0.05),表明超聲參數(shù)、聯(lián)合參數(shù)的BP人工神經(jīng)網(wǎng)絡(luò)模型預(yù)測胎兒體重的準(zhǔn)確性高于回歸方程法。(3)在非巨大兒組、整體組中,聯(lián)合參數(shù)的BP人工神經(jīng)網(wǎng)絡(luò)模型預(yù)測胎兒體重的絕對誤差、相對誤差均小于臨床參數(shù)、超聲參數(shù)的BP人工神經(jīng)網(wǎng)絡(luò)模型(P0.05);在巨大兒組中,聯(lián)合參數(shù)、超聲參數(shù)的BP人工神經(jīng)網(wǎng)絡(luò)模型預(yù)測胎兒體重的絕對誤差、相對誤差小于臨床參數(shù)的BP人工神經(jīng)網(wǎng)絡(luò)模型(P0.05),但二者之間差異無統(tǒng)計學(xué)意義(P0.05),表明在各組中,聯(lián)合參數(shù)BP人工神經(jīng)網(wǎng)絡(luò)模型預(yù)測胎兒體重準(zhǔn)確性最高。5各參數(shù)預(yù)測巨大兒的ROC曲線分析:當(dāng)宮高的取值為35.5cm,預(yù)測巨大兒的靈敏度、特異度為73.7%、82.2%;當(dāng)TCD的取值為5.34cm,預(yù)測巨大兒的靈敏度、特異度為85.4%、92.3%,表明宮高、TCD對預(yù)測巨大兒具有較高的靈敏度及特異度。6巨大兒相關(guān)因素分析:高水平血糖(OR=1.440,95%CI 1.063~1.950,P0.05)、高水平甘油三酯(OR=1.212,95%CI 1.068~1.375,P0.05)、高孕婦體重指數(shù)(OR=1.208,95%CI 1.113~1.742,P0.05)、高孕期增重指數(shù)(OR=1.113,95%CI 1.013~1.223,P0.05)是出現(xiàn)巨大兒的危險因素,高水平LDLC(OR=0.625,95%CI 0.431~0.908,P0.05)是出現(xiàn)巨大兒的保護(hù)因素。結(jié)論1應(yīng)用已有的方程預(yù)測胎兒體重時,應(yīng)根據(jù)胎兒體重范圍選擇合適方程。2在各個胎兒體重范圍內(nèi),New Equation 3能準(zhǔn)確預(yù)測胎兒出生體重。3預(yù)測胎兒體重的最佳模型為高訓(xùn)練樣本數(shù)的聯(lián)合參數(shù)BP人工神經(jīng)網(wǎng)絡(luò)模型。4預(yù)測巨大兒發(fā)生最佳的臨床及超聲指標(biāo):宮高、TCD。5出現(xiàn)巨大兒的危險因素:高水平血糖、高水平甘油三酯、孕婦高體重指數(shù)、孕期高增重指數(shù);而高水平LDLC是保護(hù)因素。
[Abstract]:Objective to investigate the ultrasound examination in predicting the best model of fetal birth weight based on the analysis of clinical parameters of childhood; looking for great critical reference value of ultrasonic parameters; analysis for risk factors of macrosomia. Methods from October 2015 to 2017 01 months in North China Polytechnic University hospital obstetrics hospital aboutsingletonnulliparousvertex full-term pregnant women in 407 cases. All fetal ultrasonography in prenatal 0-5 days. 407 fetuses were divided into two groups: group A (non macrosomia group FW4000g, 337 cases); group B (macrosomia group FW = 4000g), including 70 cases. Ultrasound measurement parameters: biparietal diameter -BPD, occipitofrontal diameter -OFD, head -HC, transverse cerebellar diameter -TCD -LL, the length of liver, abdominal diameter, abdominal diameter, abdominal circumference and femur length of -AC, -FL and femoral subcutaneous soft tissue thickness of -FSTT. Clinical parameters were collected: maternal height, body weight, weight gain during pregnancy, pregnancy, uterine height, abdominal circumference, and biochemical indexes by Excel. 2013 to establish a database, SPSS 20 for statistical analysis, the normal distribution of the data in ((?) + s) said that the skewness data to the median (four percentile interval). Correlation analysis of the parameters and the fetal weight by Pearson correlation analysis; the establishment of a new prediction of fetal weight regression equation by multiple linear regression analysis method; the measurement data were analyzed by using the single factor variance analysis, t test, rank sum test between the 22 groups were compared using LSD test, count data were compared by the chi square test; the parameter prediction of macrosomia to determine the critical reference values by ROC curve analysis; analysis of risk factors of macrosomia using Logistic regression analysis on P0.05; there was a significant difference between the results of the 19 regression equation 1. The prediction of the comparison of the accuracy of fetal weight: (1) in the 5 clinical parameters in the equation, the non macrosomia group and the whole group by Zhuo Jingru 娉，

本文編號：1655610