File Download
  Links for fulltext
     (May Require Subscription)

Conference Paper: Predictive model for fibrosis and cirrhosis in chronic hepatitis B using liver stiffness measurement

TitlePredictive model for fibrosis and cirrhosis in chronic hepatitis B using liver stiffness measurement
Authors
Issue Date2007
PublisherJohn Wiley & Sons, Inc. The Journal's web site is located at http://www.hepatology.org/
Citation
The 58th Annual Meeting of the American Association of the Study of Liver Diseases (AASLD), Boston, MA., 2-6 November 2007. In Hepatology, 2007, v. 46 suppl. S1, p. 651A-652A, abstract no. 933 How to Cite?
AbstractAIM: To correlate liver stiffness measurements with demographic and laboratory parameters, and assess the predictive value of these parameters as non-invasive markers of fibrosis and cirrhosis in chronic hepatitis B (CHB). METHODS: Transient elastography was performed in 265 CHB patients using Fibroscan (Echosens, France). Liver stiffness scores of >8.1 and >10.3 kPa were used as cut-off values for significant fibrosis and cirrhosis respectively. To derive a new index using commonly measured laboratory markers, the study sample was randomly split into a training set and a validation set. RESULTS: Of the 265 patients, 170(64%) were male. Ninety-six patients (36%) were HBeAg-positive, and 90 patients (34%) had severe fibrosis or cirrhosis. The median liver stiffness score was 6.3 kPa (range, 3.0 to 56.3). Liver stiffness correlated positively with ALT, bilirubin and AFP, and negatively with albumin levels. Using 13 parameters (age, sex, platelet, AST, ALT, GGT, AFP, albumin, globulin, bilirubin, ALP, HBV DNA, and HBeAg), the sequence of variables in order of their associations with liver stiffness (co-effi cient path) was determined using L1 regularized regression. The AUROC were calculated for each number of variables used for the prediction of fibrosis and cirrhosis, and the number of variables used was determined as the one that additional variables would not give a relatively higher accuracy. Four best predictive parameters (AST, Platelet, GGT and AFP) were used to derive a model for fibrosis (the APGA index). Additional variables did not improve accuracy significantly. The model using log(index)=1.44 + 0.1490(GGT) + 0.3308 log(AST) – 0.5846 log(platelets) + 0.1148 log (AFP+1) to predict significant fibrosis had an AUROC of 0.85 in both training and validation groups. An optimal cutoff value of 6.9 had sensitivity of 82%, specificity of 69%, with negative predictive value of 91%. The AUROC for predicting cirrhosis were 0.89 and 0.85 in training and validation groups respectively. Using an optimal cutoff of 8.9 was associated with sensitivity of 64% and specificity of 89% with a negative predictive value of 92%. The AUROC for the APGA index was higher compared to that of the AST-toplatelet ratio index, AST/ALT ratio, and age-platelet index in the training group (0.85, 0.81, 0.50, and 0.77 respectively) and the validation group (0.85, 0.80. 0.38 and 0.68 respectively) CONCLUSION: Measurement of liver stiffness with transient elastography correlates well with known factors associated with the severity of disease in Asian CHB patients. The APGA index is a reliable non-invasive method for predicting significant fibrosis and cirrhosis in CHB.
Persistent Identifierhttp://hdl.handle.net/10722/101426
ISSN
2015 Impact Factor: 11.711
2015 SCImago Journal Rankings: 4.752

 

DC FieldValueLanguage
dc.contributor.authorFung, JYYen_HK
dc.contributor.authorLai, CLen_HK
dc.contributor.authorFong, DYTen_HK
dc.contributor.authorYuen, JCHen_HK
dc.contributor.authorWong, DKHen_HK
dc.contributor.authorYuen, RMFen_HK
dc.date.accessioned2010-09-25T19:49:11Z-
dc.date.available2010-09-25T19:49:11Z-
dc.date.issued2007en_HK
dc.identifier.citationThe 58th Annual Meeting of the American Association of the Study of Liver Diseases (AASLD), Boston, MA., 2-6 November 2007. In Hepatology, 2007, v. 46 suppl. S1, p. 651A-652A, abstract no. 933en_HK
dc.identifier.issn0270-9139en_HK
dc.identifier.urihttp://hdl.handle.net/10722/101426-
dc.description.abstractAIM: To correlate liver stiffness measurements with demographic and laboratory parameters, and assess the predictive value of these parameters as non-invasive markers of fibrosis and cirrhosis in chronic hepatitis B (CHB). METHODS: Transient elastography was performed in 265 CHB patients using Fibroscan (Echosens, France). Liver stiffness scores of >8.1 and >10.3 kPa were used as cut-off values for significant fibrosis and cirrhosis respectively. To derive a new index using commonly measured laboratory markers, the study sample was randomly split into a training set and a validation set. RESULTS: Of the 265 patients, 170(64%) were male. Ninety-six patients (36%) were HBeAg-positive, and 90 patients (34%) had severe fibrosis or cirrhosis. The median liver stiffness score was 6.3 kPa (range, 3.0 to 56.3). Liver stiffness correlated positively with ALT, bilirubin and AFP, and negatively with albumin levels. Using 13 parameters (age, sex, platelet, AST, ALT, GGT, AFP, albumin, globulin, bilirubin, ALP, HBV DNA, and HBeAg), the sequence of variables in order of their associations with liver stiffness (co-effi cient path) was determined using L1 regularized regression. The AUROC were calculated for each number of variables used for the prediction of fibrosis and cirrhosis, and the number of variables used was determined as the one that additional variables would not give a relatively higher accuracy. Four best predictive parameters (AST, Platelet, GGT and AFP) were used to derive a model for fibrosis (the APGA index). Additional variables did not improve accuracy significantly. The model using log(index)=1.44 + 0.1490(GGT) + 0.3308 log(AST) – 0.5846 log(platelets) + 0.1148 log (AFP+1) to predict significant fibrosis had an AUROC of 0.85 in both training and validation groups. An optimal cutoff value of 6.9 had sensitivity of 82%, specificity of 69%, with negative predictive value of 91%. The AUROC for predicting cirrhosis were 0.89 and 0.85 in training and validation groups respectively. Using an optimal cutoff of 8.9 was associated with sensitivity of 64% and specificity of 89% with a negative predictive value of 92%. The AUROC for the APGA index was higher compared to that of the AST-toplatelet ratio index, AST/ALT ratio, and age-platelet index in the training group (0.85, 0.81, 0.50, and 0.77 respectively) and the validation group (0.85, 0.80. 0.38 and 0.68 respectively) CONCLUSION: Measurement of liver stiffness with transient elastography correlates well with known factors associated with the severity of disease in Asian CHB patients. The APGA index is a reliable non-invasive method for predicting significant fibrosis and cirrhosis in CHB.-
dc.languageengen_HK
dc.publisherJohn Wiley & Sons, Inc. The Journal's web site is located at http://www.hepatology.org/en_HK
dc.relation.ispartofHepatologyen_HK
dc.rightsHepatology. Copyright © John Wiley & Sons, Inc.en_HK
dc.titlePredictive model for fibrosis and cirrhosis in chronic hepatitis B using liver stiffness measurementen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0270-9139&volume=6&issue=4&spage=651A&epage=&date=2007&atitle=Predictive+model+for+fibrosis+and+cirrhosis+in+chronic+hepatitis+B+using+liver+stiffness+measurementen_HK
dc.identifier.emailFung, JYY: jfung@sicklehut.comen_HK
dc.identifier.emailLai, CL: hrmelcl@hku.hken_HK
dc.identifier.emailFong, DYT: dytfong@hku.hken_HK
dc.identifier.emailYuen, JCH: jchyuen@HKUCC.hku.hken_HK
dc.identifier.emailWong, DKH: danywong@hku.hken_HK
dc.identifier.emailYuen, RMF: mfyuen@hkucc.hku.hken_HK
dc.identifier.authorityFung, JYY=rp00518en_HK
dc.identifier.authorityLai, CL=rp00314en_HK
dc.identifier.authorityWong, DKH=rp00492en_HK
dc.identifier.authorityYuen, RMF=rp00479en_HK
dc.description.naturelink_to_OA_fulltext-
dc.identifier.doi10.1002/hep.22020-
dc.identifier.hkuros152469en_HK
dc.identifier.hkuros145541-
dc.identifier.volume46en_HK
dc.identifier.issuesuppl. S1en_HK
dc.identifier.spage651A, abstract no. 933en_HK
dc.identifier.epage652A-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats