本文選題：腹水 + 腫瘤標記物��； 參考：《蘭州大學(xué)》2017年碩士論文

【摘要】：背景及目的:腹水是臨床常見的一種病征,腹水可分為良性腹水及惡性腹水,良性腹水又可分為結(jié)核性腹水及非結(jié)核性良性腹水。腹水良惡性的鑒別與臨床治療方案的選擇及其預(yù)后息息相關(guān),但是部分腹水患者良惡性的鑒別在臨床中仍然很困難。目前最常用的方法仍是脫落細胞學(xué)檢查,雖具有較高的診斷特異性,但是靈敏度、準確性較低,在臨床上的應(yīng)用受到限制�，F(xiàn)急需一種簡單易行、準確率較高的診斷方法對腹水性質(zhì)進行鑒別。臨床中通過單項實驗室指標的檢測對腹水的性質(zhì)進行診斷其準確率仍低,多項指標共同檢測可提高其診斷的準確率,但過程較為復(fù)雜。本文通過對臨床病例進行回顧性研究,經(jīng)單因素分析篩選出對腹水性質(zhì)鑒別診斷有意義的實驗室指標,再通過多因素Logistic回歸分析創(chuàng)建數(shù)學(xué)診斷模型。將入選的實驗室指標作為一整體,用該數(shù)據(jù)定量來反映疾病發(fā)生可能性,以提高診斷的靈敏度、特異性和準確性。方法:本文采用回顧性分析,對2010年1月至2016年12月在蘭州大學(xué)第一醫(yī)院消化內(nèi)科住院的172例腹水患者的臨床病例資料進行分析,收集患者血清AFP、CEA、CA125、CA199、CA724、LDH、GLU、ALP、GGT、TP、ALB,腹水 AFP、CEA、CA125、CA199、CA724、LDH、GLU、ALP、GGT、TP、ADA, TP之差共23項實驗室指標,先對這些指標進行單因素分析,將有統(tǒng)計學(xué)意義的實驗指標作為白變量,分別以良、惡性腹水及結(jié)核、非結(jié)核性腹水為因變量,再進行多因素logistic回歸分析,分別建立針對惡性腹水和結(jié)核性腹水的診斷預(yù)測數(shù)學(xué)模型,并用ROC曲線對其診斷效能進行分析。結(jié)果:1.引起腹水常見的三大病因依次為肝硬化失代償期、惡性腫瘤和結(jié)核性腹膜炎。2.惡性腹水患者發(fā)病年齡要高于良性腹水患者,女性患病率多于男性;結(jié)核性腹水患者發(fā)病年齡低于非結(jié)核性腹水患者,男女的患病率無明顯差異。3.對良惡性腹水的鑒別:針對惡性腹水我們建立的診斷方程為:P1 = 1/[1 + e~(-(- 3.859 + 0.082X1+0.001X2+ 0.003X3))]其中X1 腹水 CEA,X2=腹水CA125, X3=腹水LDH,P1為預(yù)測概率,e為自然對數(shù)。數(shù)學(xué)診斷模型的ROC曲線下面積為0.944,最佳臨界值為0.515,靈敏度為77.78%,特異性為98.31%,準確性為91.86%,漏診率為22.22%,誤診率為1.69%,約登指數(shù)為76.09%,陽性預(yù)測值為95.45%,陰性預(yù)測值為90.63%,陽性似然比為46.02,陰性似然比為 0.23。4.對結(jié)核性及非結(jié)核性腹水的鑒別:針對結(jié)核性腹水我們建立的診斷方程為:P2 = 1/[1 + e~(-(-7.466-0.005X1+0.104X2 + 0.010X3 + 0.130X4))] 其中X1=腹水LDH,X2=腹水TP,X3=腹水GGT,X4=腹水ADA,P2為預(yù)測概率,e為自然對數(shù)。數(shù)學(xué)診斷模型的ROC曲線下面積為0.978,最佳臨界值為0.302,靈敏度為93.18%,特異性為94.53%,準確性為94.19%,漏診率為6.82%,誤診率為5.47%,約登指數(shù)為87.81%,陽性預(yù)測值為85.42%,陰性預(yù)測值為97.58%,陽性似然比為17.03,陰性似然比為0.07。5.對惡性腹水及結(jié)核性腹水進行綜合鑒別:如果Pre-10.515、Pre-20.302,高度懷疑為惡性腹水;Pre-10.515、Pre-20.302,高度懷疑為結(jié)核性腹水;Pre-10.515、Pre-20.302,則診斷為良性非結(jié)核性腹水;Pre-10.515、Pre-20.302,則比較哪個更接近于1即對其中之一的疾病診斷更符合。結(jié)論:運用Logistic回歸分析建立的數(shù)學(xué)診斷模型,可將入選的實驗室指標作為一整體,用定量數(shù)據(jù)對腹水性質(zhì)進行鑒別,可提高診斷的靈敏度、特異性和準確性。
[Abstract]:Background and objective: ascites is a common clinical symptom. Ascites can be divided into benign ascites and malignant ascites. Benign ascites can be divided into tuberculous ascites and non tuberculous benign ascites. The identification of benign and malignant ascites is closely related to the choice of clinical treatment scheme and its prognosis, but the differentiation of benign and malignant partial ascites is still in clinical practice. It is still difficult. The most commonly used method is still exfoliative cytology, although it has high diagnostic specificity, but sensitivity, accuracy is low, and its clinical application is limited. It is urgent to identify the properties of ascites by a simple and accurate diagnostic method. The accuracy rate of the diagnosis of ascites is still low. The common detection of multiple indexes can improve the accuracy of diagnosis, but the process is more complex. This article through a retrospective study of clinical cases, through single factor analysis to screen out the significance of the laboratory indicators for the differential diagnosis of ascites properties, and then by multi factor Logistic regression analysis to create A mathematical diagnosis model. Using the selected laboratory indicators as a whole, this data is used to reflect the possibility of the disease, in order to improve the sensitivity, specificity and accuracy of the diagnosis. Methods: a retrospective analysis was adopted in this paper for 172 cases of ascites hospitalized in the digestive department of First Hospital Affiliated to Lanzhou University from January 2010 to December 2016. 23 laboratory indexes of the patients' serum AFP, CEA, CA125, CA199, CA724, LDH, GLU, ALP, GGT, TP, ALB, and ascites are analyzed in a single factor analysis, and a statistically significant experimental index is used as a white variable, with good and malignant abdomen respectively. Water and tuberculosis, non tuberculous ascites as the dependent variables, and then multifactor logistic regression analysis, the mathematical models for the diagnosis and prediction of malignant ascites and tuberculous ascites were established respectively, and the diagnostic efficiency was analyzed with the ROC curve. Results: 1. the three common diseases caused by ascites were due to the decompensation period of cirrhosis, malignant tumor and tuberculosis. The age of the patients with.2. malignant ascites is higher than that of the benign ascites. The prevalence of female is more than that of the male; the age of the patients with tuberculous ascites is lower than that of the non tuberculous ascites. The prevalence rate of male and female is not significantly different from that of the benign and malignant ascites. The diagnostic equation for malignant ascites is: P1 = 1/[1 + e~ (- (- 3.). 859 + 0.082X1+0.001X2+ 0.003X3)) X1 ascites CEA, X2= ascites CA125, X3= ascites LDH, P1 as the prediction probability, e as the natural logarithm. The area under the ROC curve of the mathematical diagnosis model is 0.944, the optimum critical value is 0.515, the sensitivity is 77.78%, the specificity is 98.31%, the accuracy is 91.86%, the missed diagnosis rate is 22.22%, the misdiagnosis rate is 1.69% and the Jordan index is 7. 6.09%, the positive predictive value was 95.45%, the negative predictive value was 90.63%, the positive likelihood ratio was 46.02, the negative likelihood ratio was 0.23.4. for the identification of tuberculous and non tuberculous ascites. We established the diagnostic equation for tuberculous ascites: P2 = 1/[1 + e~ (- (- (- (- (- (- (- (-7.466-0.005X1+0.104X2 + 0.010X3 + 0.130X4))] in which X1= ascites LDH, X2= ascites TP, X3= Ascites GGT, X4= ascites ADA, P2 as the prediction probability, e is the natural logarithm. The area of the ROC curve under the mathematical diagnosis model is 0.978, the optimum critical value is 0.302, the sensitivity is 93.18%, the specificity is 94.53%, the accuracy is 94.19%, the missed diagnosis rate is 6.82%, the misdiagnosis rate is 5.47%, the Jordan index is 87.81%, the positive predictive value is 85.42%, the negative predictive value is 97.58%, Yang negative predictive value 97.58%, Yang is 97.58%, Yang Sexual likelihood ratio is 17.03, negative likelihood ratio is 0.07.5. for malignant ascites and tuberculous ascites. If Pre-10.515, Pre-20.302, highly suspected as malignant ascites; Pre-10.515, Pre-20.302, highly suspected as tuberculous ascites; Pre-10.515, Pre-20.302, and diagnosis of benign non tuberculous ascites; Pre-10.515, Pre-20.302, is the comparison. Which is closer to 1 is more consistent with one of the disease diagnoses. Conclusion: the mathematical diagnosis model established by Logistic regression analysis can use the selected laboratory indicators as a whole and identify the properties of ascites with quantitative data, which can improve the sensitivity, specificity and accuracy of the diagnosis.
【學(xué)位授予單位】：蘭州大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：R442.5

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