克罗恩病与溃疡性结肠炎、肠结核的临床鉴别诊断研究

发布时间：2018-05-06 07:29

  本文选题：克罗恩病 + 溃疡性结肠炎　； 参考：《浙江大学》2013年博士论文

【摘要】：目的： 在临床实践中,溃疡性结肠炎(UC)和克罗恩病(CD)的鉴别十分重要,然而常常由于临床表现不典型、模棱两可的内镜检查结果和影像表现以及肠镜下活检的深度不够,使得鉴别UC和CD成为一个难题。IBD的诊断指标包括一些生物学标记,本研究将相互独立的血清标记物作为参数进行整合,通过统计学工具和方法构建了一个用于鉴别仅结肠损伤的UC和CD的诊断模型,并进一步检验该诊断模型的效能。 并通过meta分析探讨克罗恩病(CD)与肠结核(ITB)内镜表现和组织病理学特征,为两者的鉴别诊断提供依据。 研究对象及方法： 2006年2月至2011年2月,采用回顾性分析的方法,收集了来自浙江大学医学院附属第一医院的140名UC住院患者和174名CD住院患者的资料。首次住院治疗的这段时间收集周围静脉血液样本,根据所测的血清标记物的结果,我们构建了两个逻辑回归模型。为了评估最终拟合模型的有效性,我们还用了受试者工作特征(ROC)来评估该诊断模型的预测效果,ROC曲线下面积(AUC)用来评估其准确度。 检索Pubmed、EBSCO、Web of science、中国生物医学文献数据库(the Cochrane Library and Chinese Biomedicine Database)、维普、万方数据库等数据库,时间1995年1月到2013年6月发表的关于克罗恩病和肠结核内镜表现和组织病理学特征的文献,由2名评价员独立采用QUADAS(Quality Assessment of Diagnostic AccuracyStudies)工具进行质量评价,应用Meta-disc1.4和stata12.0做异质性检验,根据异质性检验结果选择相应的效应模型合并,评价其敏感性、特异性、似然比和诊断比值比,描绘SROC曲线并计算曲线下面积,对于研究间存在较高异质性,用Meta回归分析找异质性来源,并做敏感性分析。 结果： 我们利用BIC来挑选出与疾病状态相关的预测变量。在无效模型中,利用BIC选出了预测变量Alb,TC,Plt以及Alb:Plt.在备择模型中,同样的方法选出了新的预测变量GPDA以及另加的传统预测变量TCa,两两相互作用项Alb:Plt,Alb:GPDA, TCa:TC和Plt:GPDA.CD/UC指数(CUI)结果为CUl=1.901+0.425Alb-3.324TC一7.444TCa+0.018Plt+0.087GPDA-0.0007Alb:Plt-0.004Alb:GPDA+1.839TC:TCa+0.003Plt:GPDA。UC患者的CUI大于CD患者的,CUI0则递增性倾向于UC的诊断,而CUI0则对应CD诊断的可能性更高。无效模型和备择模型的AUCs的平均值分别为0.66(95%置信区间：0.59-0.72)和0.73(95%置信区间：0.67-0.80)。截断点对应的灵敏度和特异度,备择模型中分别为0.55和0.80,而无效模型中分别为0.46和0.79。 meta分析共纳入15篇文献,包括1271个研究对象,其中克罗恩病671个,肠结核600个。统计结果显示：以克罗恩病为阳性对照,其敏感性、特异性、阳性似然比、阴性似然比、诊断比值比和SROC曲线下面积分别为：阿弗他溃疡0.39,0.80,2.20,0.75,3.34,0.7252；肠腔狭窄0.35,0.72,1.37,0.89,1.54,0.4626；鹅卵石征0.28,0.96,5.25,0.79,7.05,0.6212；跳跃征0.61,0.57,1.52,0.71,2.52,为0.6420；纵形溃疡0.42,0.94,6.18,0.65,11.02,0.7898；微肉芽肿0.42,0.69,1.42,0.82,2.08,0.5768。而以肠结核为阳性对照,环形溃疡0.43,0.88,3.66,0.64,7.07,0.7515；回盲部扩张0.38,0.91,3.98,0.74,5.98,0.8404；干酪样坏死0.42,1.00,17.10,0.69,38.25,0.9976；肉芽肿0.73,0.63,1.78,0.50,4.83,0.7268；融合肉芽肿0.41,0.99,17.74,0.60,29.86,0.9705；每个切片肉芽肿大于5个0.26,0.94,4.45,0.80,5.52,0.5702；粘膜下肉芽肿0.30,0.90,2.92,0.76,4.00,0.6559；不成比例的粘膜下炎症0.52,0.75,2.84,0.59,4.52,0.6679；肉芽组织0.31,0.92,3.68,0.72,5.23,0.8723；ulcers lined by histiocyte0.42,0.95,6.33,0.55,12.52,0.9248。 结论： 根据血清标记物的检测结果构建的CUI可成为克罗恩病和溃疡性结肠炎的鉴别诊断的辅助工具,特别是在临床病史不明,内镜和影像学特征异常,活组织检查模棱两可的情况下。 诊断性meta分析结果提示阿弗他溃疡、肠腔狭窄、鹅卵石征、跳跃征、纵形溃疡、微肉芽肿有助于诊断克罗恩病,而同时环形溃疡、回盲部扩张、干酪样坏死、肉芽肿、融合肉芽肿、每个切片肉芽肿大于5个、粘膜下肉芽肿、不成比例的粘膜下炎症和肉芽组织有助于诊断肠结核。因此,内镜结合病例组织活检的特异性表现对于鉴别克罗恩病和肠结核意义重大。
[Abstract]:Objective:
In clinical practice, the identification of ulcerative colitis (UC) and Crohn's disease (CD) is very important. However, it is often due to untypical clinical manifestations, ambiguous endoscopic findings and imaging findings, and the insufficient depth of endoscopic biopsy, making the identification of UC and CD a difficult problem of.IBD, including some biological markers, this study According to the integration of independent serum markers as parameters, a diagnostic model for identifying UC and CD for colonic damage only was constructed by statistical tools and methods, and the effectiveness of the diagnostic model was further tested.
The endoscopic and histopathological features of Crohn's disease (CD) and intestinal tuberculosis (ITB) were studied by meta analysis.
Research objects and methods:
From February 2006 to February 2011, a retrospective analysis was used to collect data from 140 UC inpatients and 174 CD inpatients from the First Affiliated Hospital of Zhejiang University medical college. The first hospitalization period collected peripheral blood samples. According to the results of the blood serum markers, we constructed two logic. Regression model. In order to evaluate the effectiveness of the final fitting model, we also used the ROC to evaluate the predictive effect of the model, and the area under the ROC curve (AUC) was used to evaluate its accuracy.
Pubmed, EBSCO, Web of science, Chinese biomedical literature database (the Cochrane Library and Chinese Biomedicine Database), VP, Wanfang database, and other databases on the features of Crohn's disease and intestinal tuberculosis endoscopes and histopathology published in January 1995 to June 2013, were independently collected by 2 evaluators. Using the QUADAS (Quality Assessment of Diagnostic AccuracyStudies) tool for quality evaluation, using Meta-disc1.4 and stata12.0 to do heterogeneity test, select the corresponding effect model combination according to the heterogeneity test results, evaluate its sensitivity, specificity, likelihood ratio and diagnostic ratio ratio, depict the SROC curve and calculate the area under the curve, There is a high heterogeneity between the studies, and Meta regression analysis is used to find the source of heterogeneity and make sensitivity analysis.
Result锛，

本文编号：1851370