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Volume 41 Issue 2
May  2018
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Article Navigation >  Journal of Molecular Imaging  > 2018  >  41(2): 233-236
Zhong WEI, Hua MAO. Predictive value of non-invasive parameter for the degree of esophageal varices in patients with post-hepatitis B cirrhosis[J]. Journal of Molecular Imaging, 2018, 41(2): 233-236. doi: 10.3969/j.issn.1674-4500.2018.02.22
Citation: Zhong WEI, Hua MAO. Predictive value of non-invasive parameter for the degree of esophageal varices in patients with post-hepatitis B cirrhosis[J]. Journal of Molecular Imaging, 2018, 41(2): 233-236. doi: 10.3969/j.issn.1674-4500.2018.02.22
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Predictive value of non-invasive parameter for the degree of esophageal varices in patients with post-hepatitis B cirrhosis

doi: 10.3969/j.issn.1674-4500.2018.02.22

  • Department of Gastroenterology, Zhujiang Hospital of Southern Medical University,Guangzhou 510282,China

  • Received Date: 2018-01-29
  • Publish Date: 2018-04-01
    Abstract
  • Objective To analysis the predictive value of transient elastography combined with serum index and liver ultrasound examination for the degree of esophageal varices (EV), and to establish a predictive model for EV. Methods A total of 110 patients with post-hepatitis B cirrhosis were enrolled and divided into two groups according to EV grade assessed by gastroscope: non-EV and grade I group and grade II and III group. The parameters, including liver stiffness (LS), platelet count (PLT), prothrombin time (PT), serum albumin level, the thickness of spleen, the portal vein diameter and the liver diameter were assessed by independent sample T test and binary logistic regression analysis, based on which a predictive model was generated. The receiver operating characteristic(ROC) curve was used to evaluate the accuracy of the model. Results The area under the ROC curve for the model was 0.979. The sensitivity of the model was 98.7% and the specificity was 97.1%. Conclusion The non-invasive model for predicting EV composed of LS and other non-invasive parameters was accurate and sensitive, thus an ideal model for EV prediction in clinic.

     

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