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胸水γ-干扰素对结核性胸腔积液诊断价值的Meta分析

  

  1. 上海市大场医院 检验科,上海 宝山 200444
  • 出版日期:2017-03-05 发布日期:2017-03-06
  • 通讯作者: 通信作者:李慧敏,Email:shlrnzyyjyk@163.com

Diagnotic value of interferongamma in pleural for tuberculous pleural effusion: a metaanalysis

  1. Department of Laboratory Medicine, Dachang Hospital of Shanghai, Shanghai 200436, China
  • Online:2017-03-05 Published:2017-03-06
  • Contact: Corresponding author:Li Huimin, Email:shlrnzyyjyk@163.com

摘要: 目的评价胸水γ干扰素(IFNγ)对结核性胸腔积液诊断价值。方法计算机检索CNKI、万方医学、pubmed、The Cochrane library、CBM和EMbase等数据库,收集关于胸水中IFNγ与结核性胸腔积液的病例和对照研究,并进行质量评价。检索时限均从建库至2016年7月,采用Cochrane协作网提供的MetaDisc软件进行Meta分析并进行异质性评价,最终纳入27篇文献。结果Meta分析结果显示胸水IFNγ诊断结核性胸腔积液的合并敏感度(SEN)为0.91(95%CI=0.89~0.93);合并特异度(SPE)为0.96(95%CI=0.95~0.97);合并阳性似然比(+LR)为20.26(95%CI=13.16~31.19);合并阴性似然比(-LR)为0.1(95%CI=0.08~0.14);合并诊断优势比(DOR)为250.8(95%CI=138.7~453.41);汇总受试者工作曲线(SROC)下面积(AUC)为0.9816。结论胸水γ干扰素对结核性胸腔积液具有较高诊断价值。

关键词: 胸腔积液, 结核, 干扰素&gamma, 诊断, meta分析

Abstract: ObjectiveTo  evaluate the value of interferon gamma(IFNγ) measurements in the diagnosis of tuberculous pleural effusion. MethodsA systematic review was conducted in  CNKI, WANFANG DATA, Pubmed, Cochrane library, CBM and EMbase to identify studies on the evaluation of the diagnosis accuracy of  IFNγ for tuberculous pleurisy effusion from inception to July 2016. Metaanalysis was performed using MetaDisc software provided by Cochrane Collaboration, and the heterogeneity was evaluated. Finally, 27 papers were included. ResultsThe summary estimates for IFNγ in the diagnosis of tuberculous pleurisy effusion were sensitivity (SEN) 0.91(95%CI  0.890.93), specidicity 0.96(95%CI=0.950.97), positive likelihood ratio (+LR) 20.26(95%CI  13.1631.19), negative likelihood ratio (-LR) 0.1(95%CI  0.080.14),diagnostic odds ratio 250.8(95%CI  138.7453.41) and the area under the SROC was 0.9816. ConclusionIFN γ  in pleural effusion plays a valuable role in the diagnosis of tuberculous pleurisy effusion.

Key words: pleural effusion, tuberculosis, interferongamma, diagnosis, meta analysis