發(fā)布時間：2018-01-13 21:03

  本文關(guān)鍵詞：心臟自主神經(jīng)病變診斷評估、相關(guān)危險(xiǎn)因素分析及數(shù)學(xué)模型構(gòu)建研究　出處：《復(fù)旦大學(xué)》2014年博士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 心臟自主神經(jīng)病變 診斷實(shí)驗(yàn) 無金標(biāo)準(zhǔn) Bayesian估計(jì) 危險(xiǎn)因素 Logistic回歸 基因-環(huán)境因素交互作用 篩查模型 人工神經(jīng)網(wǎng)絡(luò) 風(fēng)險(xiǎn)模型

【摘要】：研究背景心臟自主神經(jīng)病變通常被認(rèn)為是糖尿病比較常見的慢性并發(fā)癥之一,其發(fā)病機(jī)制尚不十分明確�，F(xiàn)已證實(shí),老年人、冠心病患者、高血壓病患者及全身免疫系統(tǒng)疾病患者亦常并發(fā)現(xiàn)心臟自主神經(jīng)病變。心臟自主神經(jīng)病變具有較高的患病率和發(fā)病率。心臟自主神經(jīng)病變診斷沒有真正的金標(biāo)準(zhǔn)存在。Ewing's試驗(yàn)為半定量試驗(yàn),被用于評價心臟自主神經(jīng)功能的方法。心率變異性檢測是通過記錄一定長度的心電圖,計(jì)算正常竇性心率相鄰心搏之間R-R間期的差異程度來反映心率的變化,定量地反映自主神經(jīng)功能。心率變異性分析方法具有量化、簡單可行、客觀、安全等優(yōu)點(diǎn)。無金標(biāo)準(zhǔn)試驗(yàn)下Bayesian方法可以評估診斷實(shí)驗(yàn)的性能(如敏感性和特異性)。然而,目前在中國人群中無金標(biāo)準(zhǔn)試驗(yàn)下Bayesian方法評估基于心率變異性標(biāo)準(zhǔn)值心臟自主神經(jīng)病變診斷性能研究未見報(bào)道。人類復(fù)雜疾病的發(fā)病模型可認(rèn)為是多基因疾病機(jī)制,根據(jù)其復(fù)雜疾病模型,心臟自主神經(jīng)病變應(yīng)該由環(huán)境因素、遺傳因素及遺傳與環(huán)境因素交互作用決定。遺傳流行病學(xué)的大量證據(jù)表明心臟自主神經(jīng)病變是遺傳因素和環(huán)境因素共同導(dǎo)致的結(jié)果。全基因組關(guān)聯(lián)分析方法發(fā)現(xiàn)并鑒定了大量與心臟自主神經(jīng)病變關(guān)聯(lián)的遺傳變異。但是中國人群的相關(guān)研究鮮有報(bào)道,更未見基因-環(huán)境交互作用的分析報(bào)道。數(shù)學(xué)模型是使用數(shù)學(xué)語言描述和抽象事物,是關(guān)于部分現(xiàn)實(shí)世界和為一種特殊目的而作的一個抽象的、簡化的結(jié)構(gòu)。數(shù)學(xué)模型在臨床中的應(yīng)用通常包括：疾病的篩查模型,風(fēng)險(xiǎn)評估模型及預(yù)后模型。疾病定量評估和預(yù)測是運(yùn)用統(tǒng)計(jì)方法和數(shù)學(xué)模型,通過對過去一些歷史數(shù)據(jù)的統(tǒng)計(jì)分析,對事物未來的發(fā)展趨勢、增減速度以及可能達(dá)到的發(fā)展水平做出數(shù)量的說明,并且以數(shù)學(xué)模型來表達(dá)基本規(guī)律,對目前狀況的評估及對未來發(fā)展進(jìn)行預(yù)測。然而,目前沒有發(fā)現(xiàn)心臟自主神經(jīng)病變相關(guān)的篩查和風(fēng)險(xiǎn)數(shù)學(xué)模型構(gòu)建研究。目的本研究目的：1)明確基于短程頻率心率變異性標(biāo)準(zhǔn)值的心臟自主神經(jīng)病變診斷價值評估；2)明確中國人群中心臟自主神經(jīng)病變相關(guān)危險(xiǎn)因素(環(huán)境、遺傳相關(guān)因素)；3)構(gòu)建心臟自主神經(jīng)病變篩查模型；及4)構(gòu)建心臟自主神經(jīng)病變風(fēng)險(xiǎn)模型。方法和結(jié)果建立中國人群心臟自主神經(jīng)病變樣本數(shù)據(jù)庫。以多階段抽樣方法(整群抽樣及簡單隨機(jī)抽樣)征集2092例樣本,完成一般數(shù)據(jù)、生化檢查、糖耐量試驗(yàn)、心率變異性檢查等臨床表型數(shù)據(jù)收集,提取DNA樣本。主要基于橫斷面研究設(shè)計(jì)的有關(guān)心臟自主神經(jīng)病變的診斷標(biāo)準(zhǔn)評估,相關(guān)危險(xiǎn)因素分析,篩查模型及其風(fēng)險(xiǎn)評估模型構(gòu)建研究。1)無金標(biāo)準(zhǔn)診斷試驗(yàn)下Bayesian分析對心臟自主神經(jīng)病變診斷評估研究：該研究基于社區(qū)的大型橫斷面數(shù)據(jù),研究人群包括2,092受試者,所有受試者完成相應(yīng)的基線數(shù)據(jù)收集和短程頻率心率變異性測試。同時我們從另一個人群中征集88名同時接受短程頻率心率變異性測試和Ewing's試驗(yàn)的受試者。首先從2092個樣本人群中選取所有的健康人(371名)明確短程頻率心率變異性各組分的參考值。在無診斷金標(biāo)準(zhǔn)情況下,應(yīng)用Bayesian方法在兩個樣本中估計(jì)基于短程頻率心率變異性檢測心臟自主神經(jīng)病變診斷性能。結(jié)果顯示短程頻率心率變異性診斷模型具有很高靈敏度(80%)和特異性(80%)。非劣效性檢測表明,短程頻率心率變異性的診斷價值并不遜色于Ewing's試驗(yàn)。普通人群中心臟自主神經(jīng)病變患病率估計(jì)為14.92%。在糖尿病患者中,其患病率估計(jì)為29.17%。2)中國人群心臟自主神經(jīng)病變危險(xiǎn)因素分析：在心臟自主神經(jīng)病變樣本數(shù)據(jù)庫中,基于診斷評估分析結(jié)果,分類患病人群何非患病人群；以Logistic回歸模型對收集的臨床表型數(shù)據(jù)進(jìn)行多因素相關(guān)分析,篩選心臟自主神經(jīng)病變環(huán)境相關(guān)危險(xiǎn)因素；將心臟自主神經(jīng)病變樣本DNA進(jìn)行SNP分型,獲取遺傳相關(guān)數(shù)據(jù),進(jìn)行基因關(guān)聯(lián)分析；在心臟自主神經(jīng)病變樣本數(shù)據(jù)庫中抽取心臟自主神經(jīng)病變患者數(shù)據(jù)進(jìn)行基因-環(huán)境交互作用分析。單因素分析顯示,14個風(fēng)險(xiǎn)因素與心臟自主神經(jīng)病變顯著關(guān)聯(lián)(P0.05)。多因素Logistic回歸分析5個獨(dú)立的危險(xiǎn)因素：年齡(OR=1.47,95%C1:1.22-1.69, P0.001,表3),心率(OR=2.41,95%CI：2.04-2.71, P 0.001),高血壓病病程(O R=1.24,95%CI:1.08-1.41, P0.05),胰島素抵抗指數(shù)(OR=3.45,95%CI:2.12-5.82, P 0.001)和腰圍(OR=3.60,95%CI：1.12-6.25,P0.001)。而在本樣本中,基因-表型分析表明所選取5個候選基因與心臟自主神經(jīng)病變無明顯關(guān)聯(lián)性,而基因-環(huán)境因素交互作用分析表明肥胖表型體重指數(shù)與SANIOA (rs7375036)存在交互作用(ORGEI= 5.404,95%CI:1.355-21.558,P=0.017)；糖尿病與SANIOA (rs7375036)存在交互作用(ORGEI=3.453,95%CI:0.973-12.254,P=0.055)；及代謝綜合征與ESRI (rs9340799)存在交互作用(ORGEI=1.505,95%CI：0.98-2.312,P=0.062)。3)中國人群心臟自主神經(jīng)病變篩查模型構(gòu)建：總樣本(2092例樣本)被平分為模型生成集和驗(yàn)證集。篩查模型在模型生成集中,由逐步多元Logistic回歸分析生成。最終回歸模型的變量為心臟自主神經(jīng)病變篩查模型的組成部分。篩查模型的性能在驗(yàn)證集和總樣本中進(jìn)行評估。最終的篩查模型變量包括年齡、體重指數(shù)、高血壓病和心率,這些變量與心臟自主神經(jīng)病變存在顯著相關(guān)性(P0.05)。模型性能分析表明,在模型的生成集和驗(yàn)證集中,其系統(tǒng)的ROC曲線下面積分別為0.726(95%CI為0.686-0.766)和0.784(95%CI為0.749-0.818)。在驗(yàn)證集中,最佳臨界分?jǐn)?shù)為6(風(fēng)險(xiǎn)積分分?jǐn)?shù)范圍是0-15),該風(fēng)險(xiǎn)評分系統(tǒng)的敏感性,特異性和需要后續(xù)標(biāo)準(zhǔn)測試的比例分別為74.63%、67.50%和39.88%。4)心臟自主神經(jīng)病變風(fēng)險(xiǎn)模型構(gòu)建及對比研究：研究目的是應(yīng)用人工神經(jīng)網(wǎng)絡(luò)和多因素Logistic回歸模型在自然人群中構(gòu)建心臟自主神經(jīng)病變的風(fēng)險(xiǎn)模型,并用比較這兩種方法所構(gòu)建的風(fēng)險(xiǎn)模型的相關(guān)性能。將研究樣本分為模型生成集和驗(yàn)證集。在同一模型生成集中分別應(yīng)用神經(jīng)網(wǎng)絡(luò)和L ogistic回歸模型分析構(gòu)建相應(yīng)的心臟自主神經(jīng)病變風(fēng)險(xiǎn)模型,并在相同的驗(yàn)證集進(jìn)行評估和預(yù)測性能分析。最后將這兩類風(fēng)險(xiǎn)模型的性能進(jìn)行比較。單因素分析顯示,14個風(fēng)險(xiǎn)因素與心臟自主神經(jīng)病變顯著關(guān)聯(lián)(P0.05)。Logistic回歸構(gòu)建風(fēng)險(xiǎn)模型的ROC曲線下面積為0.758(95%CI為0.724-0.793),神經(jīng)網(wǎng)絡(luò)風(fēng)險(xiǎn)模型ROC曲線下面積為0.762(95%CI為0.732-0.793。結(jié)論：研究表明1)短程頻率心率變異性參考值可應(yīng)用于心臟自主神經(jīng)病變診斷測試,并具有較高靈敏度和特異性。短程頻率心率變異性測試對心臟自主神經(jīng)病變的診斷價值不劣于傳統(tǒng)的Ewing's試驗(yàn),可以應(yīng)用于心臟自主神經(jīng)病變的診斷,特別適合于大規(guī)模人群診斷應(yīng)用。心臟自主神經(jīng)病變在中國人群中具有較高患病率,并在糖尿病、高血壓和代謝綜合征患者中的患病率更高。2)多因素Logistic回歸分析表明心臟自主神經(jīng)病變的環(huán)境相關(guān)危險(xiǎn)因素表明年齡、心率、高血壓病病程及代謝性因素(腰圍和胰島素抵抗指數(shù))與該疾病相關(guān)。在本樣本中,基因-表型分析表明候選基因與心臟自主神經(jīng)病變無明顯的相關(guān)性,但是基因-環(huán)境交互分析表明SCNIOA和ESR1基因與代謝性因素存在交互作用。3)我們開發(fā)了基于一組簡單變量(不需要實(shí)驗(yàn)室檢查或復(fù)雜的臨床檢查)的心臟自主神經(jīng)病變的篩查模型。該模型是一種簡單、快速、便宜、非侵入性的、可靠的篩查工具。在中國人群中,可應(yīng)用于該疾病的早期預(yù)防,用以延緩疾病的進(jìn)展。4)本研究有效的應(yīng)用人工神經(jīng)網(wǎng)絡(luò)和Logistic回歸構(gòu)建具有更高區(qū)分度和精準(zhǔn)度的心臟自主神經(jīng)病變風(fēng)險(xiǎn)模型,非劣效性檢測發(fā)現(xiàn)人工神經(jīng)網(wǎng)絡(luò)預(yù)測模型的靈敏度,特異性和預(yù)測值不劣于Logistic回歸構(gòu)建的風(fēng)險(xiǎn)模型。表明這兩類風(fēng)險(xiǎn)模型都是有效的評估和預(yù)測工具。
[Abstract]:The research background of cardiac autonomic neuropathy is often considered one of the more common chronic complications of diabetes, its pathogenesis is still unclear. It has been confirmed that elderly patients with coronary heart disease, and patients with hypertension and systemic diseases of the immune system is often found and cardiac autonomic neuropathy. Cardiac autonomic neuropathy has a high prevalence and incidence the rate of diagnosis of cardiac autonomic neuropathy. There is no real gold standard.Ewing's test for semi quantitative test method to be used for evaluation of cardiac autonomic function. HRV detection is by ECG recording length and calculate the degree of difference between normal sinus heart rate adjacent cardiac R-R interval to reflect the changes in heart rate, reflect the autonomic nerve function. The heart rate variability analysis method is simple and feasible, quantitative, objective, security and other advantages. No gold standard test Bayesian method can evaluate the performance of diagnostic tests (such as sensitivity and specificity). However, at present in the China crowd no gold standard test method of Bayesian assessment of heart rate variability in the standard value of cardiac autonomic neuropathy diagnosis performance has not been reported. Based on the complex human disease model of disease disease is considered to be a mechanism of polygenic disease, according to the the model of complex diseases, cardiac autonomic neuropathy should be decided by environmental factors, genetic factors and the interaction of genetic and environmental factors. A lot of evidence of Genetic Epidemiology showed that cardiac autonomic neuropathy is a common cause of genetic and environmental factors. The results of a genome-wide association analysis method and found substantial genetic variability and cardiac autonomic neuropathy associated. Identification of Chinese population. But there are few reports on related analysis reports, no more gene environment interaction. The model is the use of mathematical language to describe and abstract things, is on the part of the real world and for a special purpose of an abstract, simplified structure. The application of mathematical model in clinical practice usually includes: screening models of disease, risk assessment model and the prognosis of disease model. The quantitative assessment and prediction is the use of statistics method and mathematical model of the past through some statistical analysis of historical data, the development trend of the things, and increase or decrease the speed may reach the development level of the number of instructions to make, and the expression of basic rules on the mathematical model, the assessment of the current situation and predict the future development. However, there is no research on the construction of screening found the mathematical model and the risk of cardiac autonomic neuropathy related. Purpose: the purpose of this study: 1) based on the standard of God clear cardiac autonomic heart rate variability short frequency value The diagnostic value evaluation; 2) determine the related risk China center crowd dirty autonomic neuropathy (environmental factors, genetic factors); 3) construction of cardiac autonomic neuropathy screening model; and 4) the construction of cardiac autonomic neuropathy risk model. Methods and results to establish China groups of cardiac autonomic neuropathy in multistage sample database. Sampling methods (cluster sampling and simple random sampling) for 2092 samples, the completion of the general data, biochemical examination, glucose tolerance test, heart rate variability examination of clinical phenotype data collection, extraction of DNA samples. The main diagnostic criteria of cross-sectional study about design of cardiac autonomic neuropathy based on the assessment, analysis of the related risk factors. Study on.1 model screening model and risk assessment) no gold standard diagnostic test Bayesian analysis and evaluation study on the diagnosis of cardiac autonomic neuropathy: the study Large cross section data based on community, the study population consisted of 2092 subjects, all subjects completed baseline data collection and short frequency of heart rate variability in the corresponding test. At the same time we from another group of 88 to solicit and accept short frequency heart rate variability test and Ewing's test subjects. The first selection of healthy people all from a sample of 2092 people (371) were clear HRV frequency short-range reference value. In the absence of gold standard in the diagnosis of cases, the application of Bayesian method to estimate the frequency of short-range HRV detection of cardiac autonomic neuropathy diagnosis based on performance in two samples. The results showed that the frequency of short heart rate variability diagnosis model it has a very high sensitivity (80%) and specificity (80%). The results showed noninferiority testing, short frequency of heart rate variability in diagnostic value is inferior to Ewing's in the general population test. Cardiac autonomic neuropathy prevalence estimates of 14.92%. in patients with diabetes, the prevalence rate is estimated at 29.17%.2) analysis of the risk of cardiac autonomic neuropathy factors Chinese crowd: in cardiac autonomic neuropathy sample database, diagnosis and evaluation based on the results of the analysis, classification of population where the prevalence of non Logistic regression; clinical phenotype data collection model multi factor correlation analysis, to screen the environmental risk of cardiac autonomic neuropathy factors; the cardiac autonomic neuropathy samples DNA SNP types, access to genetic data, genetic correlation analysis; data extraction in patients with cardiac autonomic neuropathy in cardiac autonomic neuropathy in the sample database for analysis of gene environment interaction by single factor analysis. Show that the 14 risk factors associated with cardiac autonomic neuropathy (P0.05). Multivariate Logistic Regression analysis of 5 independent risk factors: age (OR=1.47,95%C1:1.22-1.69, P0.001, table 3), heart rate (OR=2.41,95%CI:2.04-2.71, P 0.001), hypertension (O R=1.24,95%CI:1.08-1.41 P0.05), insulin resistance index (OR=3.45,95%CI:2.12-5.82, P 0.001) and waist circumference (OR=3.60,95%CI:1.12-6.25, P0.001). In this sample, genotype phenotype analysis show that the selected 5 candidate genes and cardiac autonomic neuropathy had no obvious relevance, and gene environment interaction analysis showed that BMI and obesity phenotypes of SANIOA (rs7375036) interaction (ORGEI= 5.404,95%CI:1.355-21.558, P=0.017); diabetes mellitus (rs7375036) and SANIOA interaction (ORGEI=3.453,95%CI:0.973-12.254, P=0.055); and the metabolic syndrome ESRI (rs9340799) and interaction (ORGEI= 1.505,95%CI:0.98-2.312, P=0.062).3) China people heart The dirty construction of autonomic neuropathy screening model: the total sample (2092 samples) were divided into model generation and validation set. The screening model generation focused on model analysis. By stepwise Logistic regression to generate part of the final regression model variables for cardiac autonomic neuropathy screening model. The performance of the model is evaluated in the validation screening set and the total sample. The final screening model variables including age, BMI, hypertension disease and heart rate, these variables and cardiac autonomic neuropathy (P0.05). There was significant correlation analysis showed that the performance of the model and focus on generating set and validation of the model, the system under the ROC curve were 0.726 (95%CI to 0.686-0.766) and 0.784 (95%CI 0.749-0.818). In the validation set, the best critical score of 6 (0-15 risk score score range), the sensitivity of risk scoring system, and the specific needs The following standard test respectively 74.63%, 67.50% and 39.88%.4) of cardiac autonomic neuropathy risk model and comparison. The purpose of this study is the application of artificial neural network and multi factor Logistic regression model to construct risk model of cardiac autonomic neuropathy in natural populations, and the related performance risk model constructed by the two methods. The study sample was divided into model generation and validation set. Generation focus respectively using neural network and L ogistic regression model to analyze the construction of cardiac autonomic neuropathy risk model in the same model, and in the same test set to evaluate and predict the performance analysis. Finally compare the performance of the two kinds of risk models the single factor analysis showed that 14 risk factors significantly associated with cardiac autonomic neuropathy (P0.05) ROC curve regression to establish the risk model under.Logistic The area is 0.758 (95%CI 0.724-0.793), ROC curve area of neural network risk model was 0.762 (95%CI to 0.732-0.793. conclusion: 1) short frequency of heart rate variability in the reference value can be used in the diagnosis of cardiac autonomic neuropathy test, and has high sensitivity and specificity. Ewing's test frequency short heart rate variability test diagnostic value the cardiac autonomic neuropathy is not inferior to the traditional, can be used in the diagnosis of cardiac autonomic neuropathy, especially suitable for large populations. Diagnosis of cardiac autonomic neuropathy has a higher prevalence in China populations, and in diabetes, hypertension and metabolic syndrome in patients with a higher prevalence of.2 multi factor Logistic) the regression analysis shows that the environmental risk factors of cardiac autonomic neuropathy showed that age, heart rate, hypertension and metabolic factors (waist circumference and insulin Resistance index) associated with this disease. In this sample, genotype phenotype analysis indicated that the candidate gene and cardiac autonomic neuropathy showed no obvious correlation, but the gene environment interaction analysis showed that SCNIOA and ESR1 genes and metabolic factors interaction.3) we developed based on a simple set of variables (do not need clinical examination laboratory or complex) screening model of cardiac autonomic neuropathy. This model is a simple, fast, inexpensive, non-invasive and reliable screening tool. In the Chinese crowd, can be used in the early stage of the disease prevention, with the progress of.4 to delay disease) this study of the application of artificial neural network and Logistic regression of cardiac autonomic neuropathy risk models more discriminative and accurate, non inferiority detection sensitivity prediction model of artificial neural network, specificity and prediction The value is not inferior to the risk model constructed by Logistic regression. It shows that these two types of risk models are effective evaluation and prediction tools.

【學(xué)位授予單位】：復(fù)旦大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：R541

【參考文獻(xiàn)】

相關(guān)期刊論文 前1條

1 李桂平;李紅衛(wèi);彭娜;李緒嫻;鐘思干;;大劑量甲鈷胺對糖尿病患者心率變異性影響的評價[J];中國臨床神經(jīng)科學(xué);2009年05期

，

本文編號：1420515