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Conference Paper: Generation of the prediction models for the development of hepatocellular carcinoma in chronic hepatitis B and external validation by independent cohorts

TitleGeneration of the prediction models for the development of hepatocellular carcinoma in chronic hepatitis B and external validation by independent cohorts
Authors
KeywordsMedical sciences
Gastroenterology
Issue Date2011
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/jhep
Citation
The 2011 International Liver Congress cum the 46 Annual Meeting of the European Association for the Study of the Liver (EASL), Berlin, Germany, 30 March-3 April 2011, In Journal of Hepatology, 2011, v. 54 suppl. 1, p. S150-S151, abstract no. 375 How to Cite?
AbstractAIMS: Data mining analysis was used to generate predictive models for the development of HCC and to evaluate the efficacy of nucleos(t)ide analog (NUC) therapy to reduce HCC in patients at high risk. METHODS: A cohort of 588 chronic hepatitis B patients was screened for HCC (average period 7.5 years). Data mining analysis (IBMSPSS Modeler 13) was used to identify risk factors for HCC and to generate predictive models. Independent cohort in Hong Kong (cohort-H, n = 525) and in Ogaki, Japan (cohort-O, n = 576) was used for external validations. Sensitivity to identify patients at risk for HCC was compared between models and the guideline criteria for antiviral treatment. Data from 151 patients on long-term NUC therapy was applied on these models to evaluate the efficacy of NUC to prevent HCC. RESULTS: The 5 year prevalence of HCC in untreated cohort was 5.3%. Age (≥40), platelet (<150×109/L), HBVDNA (>5.8 log copies/ml), and mutations in core promoter 1762/1764 were identified as risk factors and were used to build prediction model of HCC (model-1). The 5 year prevalence of HCC in patients having 0, 1, 2, 3, and 4 risk factors was 0%, 0–3.4%, 3.2–4.3%, 17.6% and 30%, respectively. In the model without incorporating core promoter mutations (model-2), the 5 year prevalence of HCC in patients having 0, 1, 2, and 3 risk factors was 0%, 1.1–1.8%, 5.0–9.1%, and 28.1%, respectively. The external validations confirmed the reproducibility of these models (cohort-H: r2 = 0.87–0.95, cohort-O: r2 = 0.93). Sensitivity for identifying HCC development was as follows: AASLD guideline 19%, EASL guideline 67%, model-1 83%, and model-2 98%. In patients with high risk of HCC, long-term NUC therapy reduced the 5 year incidence of HCC by 11–23% (p < 0.05). CONCLUSIONS: Prediction models that include age, platelet counts, HBVDNA, and core promoter mutations had high reproducibility and sensitivity to identify patients with risk for the development of HCC. These models may be used to extract patients at risk for HCC out of those excluded from therapy by treatment guidelines. The NUC therapy significantly reduced the incidence of HCC among patients with high risk for HCC.
DescriptionThis journal suppl. is Abstract Book of the 2011 International Liver Congress™ & the 46th EASL Annual Meeting
Poster no. 375
Persistent Identifierhttp://hdl.handle.net/10722/169371
ISSN
2015 Impact Factor: 10.59
2015 SCImago Journal Rankings: 4.570

 

DC FieldValueLanguage
dc.contributor.authorKurosaki, Men_US
dc.contributor.authorYuen, MFen_US
dc.contributor.authorSeto, WKen_US
dc.contributor.authorKumada, Ten_US
dc.contributor.authorTanaka, Ken_US
dc.contributor.authorSuzuki, Yen_US
dc.contributor.authorHoshioka, Yen_US
dc.contributor.authorTarnaki, Nen_US
dc.contributor.authorKato, Ten_US
dc.contributor.authorYasui, Yen_US
dc.contributor.authorHosokawa, Ten_US
dc.contributor.authorUeda, Ken_US
dc.contributor.authorTsuchiya, Ken_US
dc.contributor.authorKuzuya, Ten_US
dc.contributor.authorNakanishi, Hen_US
dc.contributor.authorItakura, Jen_US
dc.contributor.authorTakahashi, Yen_US
dc.contributor.authorAsahina, Yen_US
dc.contributor.authorIzumi, Nen_US
dc.date.accessioned2012-10-18T08:51:57Z-
dc.date.available2012-10-18T08:51:57Z-
dc.date.issued2011en_US
dc.identifier.citationThe 2011 International Liver Congress cum the 46 Annual Meeting of the European Association for the Study of the Liver (EASL), Berlin, Germany, 30 March-3 April 2011, In Journal of Hepatology, 2011, v. 54 suppl. 1, p. S150-S151, abstract no. 375en_US
dc.identifier.issn0168-8278-
dc.identifier.urihttp://hdl.handle.net/10722/169371-
dc.descriptionThis journal suppl. is Abstract Book of the 2011 International Liver Congress™ & the 46th EASL Annual Meeting-
dc.descriptionPoster no. 375-
dc.description.abstractAIMS: Data mining analysis was used to generate predictive models for the development of HCC and to evaluate the efficacy of nucleos(t)ide analog (NUC) therapy to reduce HCC in patients at high risk. METHODS: A cohort of 588 chronic hepatitis B patients was screened for HCC (average period 7.5 years). Data mining analysis (IBMSPSS Modeler 13) was used to identify risk factors for HCC and to generate predictive models. Independent cohort in Hong Kong (cohort-H, n = 525) and in Ogaki, Japan (cohort-O, n = 576) was used for external validations. Sensitivity to identify patients at risk for HCC was compared between models and the guideline criteria for antiviral treatment. Data from 151 patients on long-term NUC therapy was applied on these models to evaluate the efficacy of NUC to prevent HCC. RESULTS: The 5 year prevalence of HCC in untreated cohort was 5.3%. Age (≥40), platelet (<150×109/L), HBVDNA (>5.8 log copies/ml), and mutations in core promoter 1762/1764 were identified as risk factors and were used to build prediction model of HCC (model-1). The 5 year prevalence of HCC in patients having 0, 1, 2, 3, and 4 risk factors was 0%, 0–3.4%, 3.2–4.3%, 17.6% and 30%, respectively. In the model without incorporating core promoter mutations (model-2), the 5 year prevalence of HCC in patients having 0, 1, 2, and 3 risk factors was 0%, 1.1–1.8%, 5.0–9.1%, and 28.1%, respectively. The external validations confirmed the reproducibility of these models (cohort-H: r2 = 0.87–0.95, cohort-O: r2 = 0.93). Sensitivity for identifying HCC development was as follows: AASLD guideline 19%, EASL guideline 67%, model-1 83%, and model-2 98%. In patients with high risk of HCC, long-term NUC therapy reduced the 5 year incidence of HCC by 11–23% (p < 0.05). CONCLUSIONS: Prediction models that include age, platelet counts, HBVDNA, and core promoter mutations had high reproducibility and sensitivity to identify patients with risk for the development of HCC. These models may be used to extract patients at risk for HCC out of those excluded from therapy by treatment guidelines. The NUC therapy significantly reduced the incidence of HCC among patients with high risk for HCC.-
dc.languageengen_US
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/jhep-
dc.relation.ispartofJournal of Hepatologyen_US
dc.subjectMedical sciences-
dc.subjectGastroenterology-
dc.titleGeneration of the prediction models for the development of hepatocellular carcinoma in chronic hepatitis B and external validation by independent cohortsen_US
dc.typeConference_Paperen_US
dc.identifier.emailKurosaki, M: kurosaki@musashino.jrc.or.jpen_US
dc.identifier.emailYuen, MF: mfyuen@hku.hken_US
dc.identifier.emailSeto, WK: wkseto2@hku.hk-
dc.identifier.authorityYuen, MF=rp00479en_US
dc.identifier.authoritySeto, WK=rp01659en_US
dc.identifier.hkuros211559en_US
dc.identifier.volume54en_US
dc.identifier.issuesuppl. 1en_US
dc.identifier.spageS150en_US
dc.identifier.epageS151en_US
dc.publisher.placeNetherlands-

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