predictive value of fibroscan on liver ascites cause by cirrhosis

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ID: 168904
2011
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Abstract
Objective To investigate the predictive value of FibroScan for liver ascites caused by cirrhosis.Methods A total of 651 patients with liver cirrhosis were subjected to FibroScan examination in People’s Liberation Army 302 Hospital from December 2009 to June 2010 and were enrolled in the present study.Among the patients,185 suffered from liver cirrhosis with ascites(all patients initially had ascites) and 466 did not suffer from ascites.After obtaining the FibroScan value,the difference in liver cirrhosis caused by chronic hepatitis B and liver cirrhosis caused by chronic hepatitis C and other liver cirrhosis were analyzed.A Receive Operating Characteristic(ROC) curve was drawn and the area under the curve(AUROC) was analyzed to determine the cutoff value,sensitivity,specificity,positive predictive value,and negative predictive value of the FibroScan for predicting ascites.Results The FibroScan value of patients with liver cirrhosis caused by chronic hepatitis C [27.0(20.6-44.3)kPa] was obviously higher than that of patients with liver cirrhosis caused by chronic hepatitis B [23.6(13.7-37.7)kPa,P < 0.01].Moreover,the average FibroScan value of the other liver cirrhosis patients was 23.8(13.7-50.1)kPa,which isn′t different from the FibroScan value of liver cirrhosis patients with chronic hepatitis C or B.The FibroScan median of the liver cirrhosis patients with ascites [45.0(33.1-69.1) kPa] was significantly higher than that of the liver cirrhosis patients without ascites [19.1(12.1-26.3) kPa,P < 0.01].The AUROC value of the FibroScan for predicting ascites was 0.895(95% CI: 0.869-0.918),the cutoff value of the diagnosis was 27.7 kPa,sensitivity was 88.2%,specificity was 81.5%,the positive predictive rate was 66.5%,and the negative predictive rate was 94.3%.Conclusion FibroScan can effectively predict the likelihood of ascites formation in patients with cirrhosis and has value for clinical application.
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Authors ;Ping HAN;Dong JI;Jian ZHANG;Fan LI;Bing LI;Qing SHAO;Guo-feng CHEN
Journal frontiers in neurorobotics
Year 2011
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