發(fā)布時間：2019-06-01 11:20

【摘要】：研究目的:急性主動脈夾層(acute aortic dissection,AAD)是一種發(fā)病突然、進展迅速、致死率高的心血管疾病;早期診斷并進行合理的治療是降低急性主動脈夾層患者死亡率的關(guān)鍵。自1761年Morgagni第一次報道以來,急性主動脈夾層的診療水平不斷進步,但其臨床誤診率仍然高達39%。目前急性主動脈夾層的確診主要是根據(jù)影像學(xué)檢查結(jié)果的單一模式,缺乏有針對性的實驗室檢驗指標(biāo)來輔助診斷。肝腎功能、血常規(guī)、凝血功能等臨床最常用檢驗項目具有檢查迅速、成本低等優(yōu)點,且已在基層醫(yī)療中心廣泛普及;更重要的是,上述檢驗項目中的血脂、白細(xì)胞計數(shù)、中性粒細(xì)胞計數(shù)、血小板計數(shù)、D-二聚體、纖維蛋白原等等眾多指標(biāo)已被眾多研究證實在急性主動脈夾層患者循環(huán)血中發(fā)生明顯改變。所以,本研究以急性主動脈夾層患者上述臨床常用檢驗項目和部分病史信息為研究對象,尋找基于臨床最常用實驗室檢驗方法的,可用于急性主動脈夾層早期診斷和病情評估的實用工具。研究方法:本研究分為模型構(gòu)建和模型評價兩個環(huán)節(jié)。在模型構(gòu)建環(huán)節(jié),按照納入和排除標(biāo)準(zhǔn),納入2014年10月至2016年8月于第二軍醫(yī)大學(xué)附屬長海醫(yī)院治療的患者共392例,其中急性主動脈夾層患者230例(包括:急性期生存患者,n=175;急性期死亡患者,n=55;Stanford A型患者,n=113;Stanford B型患者,n=113;因發(fā)病后迅速死亡未能確定Stanford分型者,n=4)和對照組患者162例(包括:急性心肌梗死[acute myocardial infarction,AMI],n=55;急性肺動脈栓塞[acute pulmonary embolism,APE],n=39;腹主動脈瘤[abdominal aortic aneurysm,AAA],n=68)。首先我們將急性主動脈夾層患者的血液檢驗指標(biāo)分別與上述3組對照組患者的血液檢驗指標(biāo)進行單因素變量分析,同時也根據(jù)急性主動脈夾層患者的急性期死亡情況和Stanford分型進行亞組間的單變量分析。通過上述分析過程,確定各組及各亞組間具有明顯差異的血液學(xué)檢驗指標(biāo)。然后將上述差異指標(biāo)納入多變量分析,確定每個指標(biāo)在多變量分析中的回歸系數(shù)、標(biāo)準(zhǔn)誤、Wald卡方值、P值、其對應(yīng)的OR值及其95%置信區(qū)間。最后將多變量分析的結(jié)果帶入R軟件,制作各對照組和亞組的列線圖,得到急性主動脈夾層與急性心肌梗死鑒別診斷模型(diagnostic model between AAD and AMI,DMDI)、急性主動脈夾層與急性肺動脈栓塞鑒別診斷模型(diagnostic model between AAD and APE,DMDE)、急性主動脈夾層與腹主動脈瘤鑒別診斷模型(diagnostic model between AAD and AAA,DMDA)、Stanford A、B型急性主動脈夾層鑒別診斷模型(diagnostic model between Stanford A AAD and Stanford B AAD,DMAB)、急性主動脈夾層患者急性期死亡預(yù)警模型(modle of death risk judgment for AAD sufferer,MDRJ)。在模型評價環(huán)節(jié),納入同期在第二軍醫(yī)大學(xué)附屬長海醫(yī)院治療的因患者159例,其中急性主動脈夾層患者60例(包括:急性期生存患者,n=34;急性期死亡患者,n=26;stanforda型患者,n=32;stanfordb型患者,n=28)和對照組患者99例(包括:急性心肌梗死[acutemyocardialinfarction,ami],n=36;急性肺動脈栓塞[acutepulmonaryembolism,ape],n=20;腹主動脈瘤[abdominalaorticaneurysm,aaa],n=43)。我們將上述不同分組患者臨床檢驗指標(biāo)帶入上述模型,利用相應(yīng)模型計算各組患者預(yù)測分?jǐn)?shù),利用診斷試驗的評價方法,計算上述五個模型的準(zhǔn)確度、靈敏度和特異度,綜合評價上述五個模型。結(jié)果:(1)通過對急性主動脈夾層患者和急性心肌梗死患者的臨床檢驗指標(biāo)和病史資料進行單因素和多因素分析,最終篩選患者年齡(age)、高密度脂蛋白(hdl)、血小板計數(shù)(plt)、d-二聚體(d-dimer)、纖維蛋白原(fib)、中性粒細(xì)胞計數(shù)與淋巴細(xì)胞計數(shù)比值(neutrophiltolymphocyteratio,nlr)、甘油三酯與高密度脂蛋白比值(triglyceride/hdl,tg/hdl)納入急性主動脈夾層與急性心肌梗死鑒別診斷模型;經(jīng)驗證該模型準(zhǔn)確率=80.49%,靈敏度=74.29%,特異度=85.11%。(2)通過對急性主動脈夾層患者和急性肺動脈栓塞患者的臨床檢驗指標(biāo)和病史資料進行單因素和多因素分析,最終篩選患者年齡(age)、吸煙史(smoke)、白細(xì)胞計數(shù)(wbc)、單核細(xì)胞計數(shù)(mono)、中性粒細(xì)胞計數(shù)(gran)和凝血酶原時間(pt)納入急性主動脈夾層與急性肺動脈栓塞鑒別診斷模型;經(jīng)驗證該模型準(zhǔn)確率=66.18%,靈敏度=61.90%,特異度=68.09%。(3)通過對急性主動脈夾層患者和腹主動脈瘤患者的臨床檢驗指標(biāo)和病史資料進行單因素和多因素分析,最終篩選患者年齡(age)、膽固醇(cholesterol)、甘油三酯(triglyceride)、低密度脂蛋白(ldl)和中性粒細(xì)胞計數(shù)(gran)納入急性主動脈夾層與腹主動脈瘤鑒別診斷模型;經(jīng)驗證該模型準(zhǔn)確率=78.31%,靈敏度=72.22%,特異度=82.98%。(4)通過對急性主動脈夾層stanforda型患者和stanfordb患者的臨床檢驗指標(biāo)和病史資料進行單因素和多因素分析,最終篩選患者年齡(age)、中性粒細(xì)胞計數(shù)(gran)、d-二聚體(d-dimer)、纖維蛋白原(fib)和活化部分凝血活酶時間(aptt)納入stanforda、b型急性主動脈夾層鑒別診斷模型;經(jīng)驗證該模型準(zhǔn)確率=72.34%,靈敏度=76.19%,特異度=69.23%。(5)通過對急性主動脈夾層急性期生存患者和死亡患者的臨床檢驗指標(biāo)和病史資料進行單因素和多因素分析,最終篩選白細(xì)胞計數(shù)(wbc)、中性粒細(xì)胞比值(neut_ratio)、血小板計數(shù)(plt)、d-二聚體(d-dimer)、患者年齡(age)納入急性主動脈夾層患者急性期死亡預(yù)警模型;經(jīng)驗證該模型準(zhǔn)確率=76.59%,靈敏度=78.13%,特異度=73.33%。結(jié)論:通過本研究,我們構(gòu)建了急性主動脈夾層與急性心肌梗死鑒別診斷模型、急性主動脈夾層與急性肺動脈栓塞鑒別診斷模型、急性主動脈夾層與腹主動脈瘤鑒別診斷模型、Stanford A、B型急性主動脈夾層鑒別診斷模型、急性主動脈夾層患者急性期死亡預(yù)警模型;上述模型可以協(xié)助臨床醫(yī)師對急性主動脈夾層患者進行快速診斷和病情評估以及急性期死亡風(fēng)險評估;對進一步完善目前急性主動脈夾層的診斷模式和豐富急性主動脈夾層患者的病情評估方法具有重要意義。
[Abstract]:The purpose of this study is that the acute aortic dissection (AAD) is a kind of cardiovascular disease with rapid onset, rapid progress and high fatality rate, and the early diagnosis and reasonable treatment is the key to reduce the mortality of the patients with acute aortic dissection. Since the first report of Morgagni in 1761, the level of diagnosis and treatment of acute aortic dissection has advanced, but its clinical misdiagnosis rate is still as high as 39%. At present, the diagnosis of the acute aortic dissection is mainly based on the single pattern of the results of the imaging examination and the lack of targeted laboratory test indicators to assist in the diagnosis. The clinical most common test items such as the liver and kidney function, the blood routine, the blood coagulation function and the like have the advantages of rapid examination, low cost and the like, and have been widely popularized in the basic medical center; and more importantly, the blood fat, the white blood cell count, the neutrophil count and the platelet count in the above test items are more important, A number of indicators such as D-dimer, fibrinogen and so on have been demonstrated to have a significant change in circulating blood in patients with acute aortic dissection. Therefore, this study is based on the clinical common test items and some medical history information of the patients with acute aortic dissection as the study object, and the utility of the early diagnosis and evaluation of the acute aortic dissection can be found based on the most common laboratory test methods. The research method: this study is divided into two parts: model construction and model evaluation. In the model building,392 patients with acute aortic dissection (including patients with acute stage survival, n = 175, and n = 55) were included in the second military medical university in August 2014 to August 2016, in accordance with the inclusion and exclusion criteria. A type of Stanford A patient, n = 113; a Stanford B-type patient, n = 113; and 162 of the patients in the control group (including: acute myocardial infarction[acute myoctal infraction, AMI], n = 55; acute pulmonary embolism[acute pulmonary, APE], n = 39; abdominal aortic aneurysm[abdominitic anaplastic, AAA],n=68). First, we compare the blood test index of the patients with acute aortic dissection with the blood test index of the 3-group control group, and the single-factor analysis between the sub-groups according to the acute death of the acute aortic dissection and the Stanford type. Hematology test indicators with significant differences between the groups and subgroups were determined by the above analysis process. And then the difference index is included in the multivariate analysis to determine the regression coefficient, the standard error, the Wald card square value, the P value, the corresponding OR value and the 95% confidence interval of each index in the multivariate analysis. and finally, the results of the multi-variable analysis are carried into the R software, and a column chart of each control group and a subgroup is manufactured to obtain an acute aortic dissection and an acute myocardial infarction differential diagnosis model (DMDI), An acute aortic dissection and an acute pulmonary embolism differential diagnosis model (DMDE), an acute aortic dissection and an abdominal aortic aneurysm differential diagnosis model (DMDA), Stanford A, Model of acute aortic dissection (AD and Stanford B AAD, DMAB) and the model of death early-warning for acute aortic dissection (MDRJ). In the model evaluation,159 patients with acute aortic dissection (including patients with acute stage survival, n = 34, patients with acute phase death, n = 26; stanford type), n = 32; stanford db, were included in the second military medical university attached to the long-sea hospital in the same period. N = 28) and in the control group,99 (including: acute myocardial infarction[acutumardihalation, ami], n = 36; acute pulmonary embolism[acutepulmonarymal, ape], n = 20; abdominal aortic aneurysm[abdominalaoricaneureysm, aaa], n = 43). In that model, the clinical examination index of the different group of patients is carry into the model, and the prediction score of each group is calculated by using the corresponding model, and the accuracy, the sensitivity and the specificity of the five models are calculated by using the evaluation method of the diagnosis test, and the five models are comprehensively evaluated. Results: (1) The age (age), high density lipoprotein (hdl) and platelet count (plt) of patients with acute aortic dissection and patients with acute myocardial infarction were analyzed by single factor and multi-factor analysis. The ratio of d-dimer (d-dimer), fibrinogen (fib), neutrophil count and lymphocyte count (nlr), triglyceride to high-density lipoprotein (tg/ hdl) was included in the differential diagnosis model of acute aortic dissection and acute myocardial infarction. The accuracy of the model was 80.49%, the sensitivity was 74.29%, and the specificity was 85.11%. (2) The patient age (age), smoking history (smoke), white blood cell count (wbc), and monocytic count (mono) were selected by single-factor and multi-factor analysis of the clinical and medical history data of patients with acute aortic dissection and acute pulmonary embolism. Neutrophil count (gran) and prothrombin time (pt) were included in the diagnosis model of acute aortic dissection and acute pulmonary embolism. The accuracy of the model was 66.18%, the sensitivity was 61.90%, and the specificity was 68.09%. and (3) finally screening the age (age), the cholesterol (cholesterol) and the triglyceride of the patients by performing single-factor and multi-factor analysis on the clinical examination indexes and the medical history data of the patients with the acute aortic dissection and the abdominal aortic aneurysm, Low density lipoprotein (ldl) and neutrophil count (gran) were included in the diagnosis model of acute aortic dissection and abdominal aortic aneurysm. The accuracy of the model was 78.31%, the sensitivity was 72.22%, and the specificity was 82.98%. (4) The patient age (age), the neutrophil count (gran), and the d-dimer (d-dimer) were selected by single-factor and multi-factor analysis of the clinical and medical history data of the patients with an acute aortic dissection and the stanford-type patient. Fibrinogen (fib) and activated partial thromboplastin time (aptt) were included in the differential diagnosis model of the type b acute aortic dissection. The accuracy of the model was 72.34%, the sensitivity was 76.19%, and the specificity was 69.23%. (5) By single-factor and multi-factor analysis of the clinical examination index and the medical history data of the patients with acute aortic dissection and the patients with death, the white blood cell count (wbc), the neut _ ratio and the platelet count (plt) were selected. D-dimer (d-dimer) and age of the patient were included in the early-stage death-warning model of acute aortic dissection; the accuracy of the model was 76.59%, the sensitivity was 78.13%, and the specificity was 73.33%. Conclusion: Through this study we constructed the diagnosis model of acute aortic dissection and acute myocardial infarction, the diagnosis model of acute aortic dissection and acute pulmonary embolism, the diagnosis model of acute aortic dissection and abdominal aortic aneurysm, Stanford A, The model can be used to assist the clinician in the rapid diagnosis and assessment of the acute aortic dissection and the assessment of the risk of death in the acute stage. It is of great significance to further improve the diagnosis model of acute aortic dissection and the method of evaluating the condition of patients with acute aortic dissection.
【學(xué)位授予單位】：第二軍醫(yī)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：R543.1

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