本文選題：妊娠 + 膽汁淤積��； 參考：《浙江大學(xué)》2014年碩士論文

【摘要】：背景 妊娠期肝內(nèi)膽汁淤積癥(intrahepatic cholestasis of pregnancy, ICP)是妊娠中晚期的特發(fā)性疾病,以皮膚瘙癢,膽汁酸升高及肝功能異常為臨床特點(diǎn)。其對孕婦的影響極小,主要危及胎兒,可引起早產(chǎn)、宮內(nèi)窘迫、羊水糞染,圍產(chǎn)兒死亡等。ICP導(dǎo)致圍產(chǎn)兒不良結(jié)局發(fā)生的機(jī)制目前尚未闡明,同時缺乏有效的防治措施,對胎兒威脅較大,也給廣大產(chǎn)科臨床工作者帶來許多困惑。 國內(nèi)外學(xué)者已進(jìn)行了一系列從實(shí)驗(yàn)室到臨床的研究,幫助判斷胎兒在子宮內(nèi)的環(huán)境,但是準(zhǔn)確性不甚理想。ICP發(fā)病時有眾多因素可能與胎兒出現(xiàn)早產(chǎn)、窘迫、死胎死產(chǎn)等有關(guān),所有單一因素均無法完全解釋胎兒出現(xiàn)這些并發(fā)癥。目前臨床工作中沒有一套有效的評估ICP孕婦胎兒宮內(nèi)環(huán)境的方法。如果把評估ICP孕婦胎兒宮內(nèi)環(huán)境狀況的參數(shù)量化并進(jìn)行有效地數(shù)據(jù)處理,相關(guān)結(jié)果用于胎兒風(fēng)險評估,將有助于合理制訂ICP治療方案、及時選擇終止妊娠時機(jī)。 近年來,在醫(yī)學(xué)領(lǐng)域興起了采用人工神經(jīng)網(wǎng)絡(luò)來處理多參數(shù)的、復(fù)雜的、相互關(guān)聯(lián)的問題,從中進(jìn)行分析、推理、識別和預(yù)測,并已取得了一定的成效。因此,我們首次將人工神經(jīng)網(wǎng)絡(luò)引入妊娠期肝內(nèi)膽汁淤積癥胎兒風(fēng)險預(yù)測體系中。目的 通過ICP孕婦生化指標(biāo)和臨床資料作為參數(shù)建立預(yù)測妊娠期肝內(nèi)膽汁淤積癥胎兒風(fēng)險的人工神經(jīng)網(wǎng)絡(luò)模型,并探討其預(yù)測價值。 方法 選取203例在浙江大學(xué)醫(yī)學(xué)院附屬婦產(chǎn)科醫(yī)院住院分娩的ICP孕婦作為研究對象,收集和篩選與胎兒風(fēng)險相關(guān)的指標(biāo)。構(gòu)建聯(lián)合參數(shù)法和生化參數(shù)法兩種人工神經(jīng)網(wǎng)絡(luò)模型,按羊水混濁比例隨機(jī)將其中135例作為訓(xùn)練集,另外68例作為測試集,分別計算預(yù)測胎兒風(fēng)險的準(zhǔn)確率,觀測兩種模型的敏感度和特異度,并繪制ROC曲線及計算曲線下面積分析預(yù)測效能。分析各個輸入?yún)?shù)對預(yù)測結(jié)果的影響權(quán)重。 結(jié)果 1、聯(lián)合參數(shù)法建立的ANN模型的預(yù)測敏感度為80%,特異度為62.2%,準(zhǔn)確率為66.2%；生化參數(shù)法建立的ANN模型的預(yù)測敏感度為73.6%,特異度為61.5%,準(zhǔn)確率為64.7%。聯(lián)合參數(shù)法ANN模型的準(zhǔn)確率、敏感度及特異度均較高于生化參數(shù)法ANN模型。 2、聯(lián)合參數(shù)法ANN模型的所有集、訓(xùn)練集及測試集的ROC曲線下面積分別為0.7991,0.8417,0.7100。生化參數(shù)ANN模型的所有集、訓(xùn)練集及測試的ROC曲線下面積分別為0.7714,0.8110,0.7036。兩種模型的預(yù)測效能為中等。 3.妊娠合并癥的影響系數(shù)最高(15.73%),其次為CG(14.63%),其余為發(fā)病孕周(11.35%),皮膚瘙癢病程(10.56%),DBIL(8.44%),S/D比值(7.24%),分娩孕周(5.67%),ALT(4.91%),年齡(4%),分娩方式(3.84%),胎數(shù)(3.78%),TBA(3.64%),NST分?jǐn)?shù)(2.91%),羊水量(2.27%),AST(0.74%),宮縮、TBIL(0.14%)。結(jié)論 1、應(yīng)用ANN技術(shù)可以全面客觀評估ICP胎兒風(fēng)險,但相關(guān)參數(shù)仍需進(jìn)一步改進(jìn)和完善。 2、聯(lián)合參數(shù)法ANN模型的準(zhǔn)確率、敏感度及特異度優(yōu)于單純生化參數(shù)法建立的ANN模型。 3、聯(lián)合參數(shù)法中參數(shù)權(quán)重在10%以上的有：妊娠合并癥(15.73%)、CG(14.63%)、發(fā)病孕周(11.35%)及皮膚瘙癢病程(10.56%)。
[Abstract]:Background

ICP ( ICP ) is an idiopathic disease in the middle and late stage of pregnancy . It is characterized by skin itching , bile acid elevation and abnormal liver function . The mechanism of ICP in pregnant women is very small , which can cause premature labor , intrauterine distress , amniotic fluid econium staining , perinatal death , etc . The mechanism of ICP leading to perinatal adverse outcome has not yet been clarified yet , meanwhile , there is a lack of effective control measures .

A series of studies from laboratory to clinic have been carried out by scholars at home and abroad to help judge the fetus ' s environment in the womb , but the accuracy is not ideal . There are many factors which may be related to the premature birth , distress , stillbirth and stillbirth of the fetus . There is no effective method to evaluate the intrauterine environment of the ICP pregnant woman .

In recent years , artificial neural networks have been used in the medical field to deal with multi - parameter , complex and interrelated problems , from which analysis , reasoning , identification and prediction have been made , and some results have been achieved . Therefore , we first introduced the artificial neural network into the fetal risk prediction system of intrahepatic calculosis of pregnancy .

Objective To establish an artificial neural network model for predicting the risk of fetal stasis in pregnancy during pregnancy by using biochemical indexes and clinical data of ICP pregnant women as parameters , and to discuss its predictive value .

method

In this paper , 203 ICP pregnant women who were hospitalized in the Affiliated Hospital of Zhejiang University Medical College were selected as the research subjects , and the indexes related to the risk of the fetus were collected and screened . 135 cases were randomly divided into training sets according to the proportion of amniotic fluid turbidity , and the sensitivity and specificity of the two models were calculated respectively .

Results

1 . The sensitivity of ANN model established by joint parameter method is 80 % , the specificity is 62.2 % , the accuracy rate is 66.2 % ;
The ANN model has a sensitivity of 73.6 % , a specificity of 61.5 % and an accuracy of 64.7 % . The accuracy , sensitivity and specificity of ANN model are higher than those of ANN model .

2 . The area of the ROC curves of all sets , training sets and test sets of the ANN model of the joint parameter method is 0.7991 , 0.8417 and 0.771 respectively . The area under the ROC curve of the ANN model is 0.7714 , 0.8110 and 0.7036 , respectively . The prediction efficiency of the two models is medium .

3 . The coefficient of pregnancy complications was the highest ( 15.73 % ) , followed by CG ( 14.63 % ) , the rest being the gestational week ( 11.35 % ) , the skin itching course ( 10.56 % ) , the birth control week ( 3.84 % ) , the fetal number ( 3.78 % ) , the TBA ( 3.64 % ) , the birth mode ( 2.91 % ) , the amniotic fluid volume ( 2.27 % ) , AST ( 0.74 % ) , uterine contraction , TBIL ( 0.14 % ) . Conclusion

1 . The risk of ICP fetus can be evaluated objectively by ANN technology , but the related parameters need to be further improved and improved .

2 . The accuracy rate , sensitivity and specificity of ANN model of combined parameter method are superior to ANN model established by simple biochemical parameter method .

3 . The weight of parameters in the joint parameter method was more than 10 % : pregnancy complications ( 15.73 % ) , CG ( 14.63 % ) , gestational week ( 11.35 % ) and skin pruritus ( 10.56 % ) .

【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：R714.255

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相關(guān)期刊論文 前3條

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