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Transient elastography-based risk estimation of hepatitis B virus-related occurrence of hepatocellular carcinoma: development and validation of a predictive model

BACKGROUND: The purpose of this study was to develop and validate a novel transient elastography-based predictive model for occurrence of hepatocellular carcinoma (HCC). METHODS: A total of 1,250 patients with chronic hepatitis B and baseline liver stiffness values were recruited between May 2005 an...

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Detalles Bibliográficos
Autores principales: Kim, Do Young, Song, Ki Jun, Kim, Seung Up, Yoo, Eun Jin, Park, Jun Yong, Ahn, Sang Hoon, Han, Kwang-Hyub
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove Medical Press 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3804604/
https://www.ncbi.nlm.nih.gov/pubmed/24204161
http://dx.doi.org/10.2147/OTT.S51986
Descripción
Sumario:BACKGROUND: The purpose of this study was to develop and validate a novel transient elastography-based predictive model for occurrence of hepatocellular carcinoma (HCC). METHODS: A total of 1,250 patients with chronic hepatitis B and baseline liver stiffness values were recruited between May 2005 and December 2007. The predictive model for HCC occurrence was constructed based on a Cox proportional hazards model. We estimated baseline disease-free probabilities at 3 years. Discrimination and calibration were used to validate the model. RESULTS: HCC occurred in 56 patients during a median follow-up of 30.7 months. Multivariate analysis revealed that age, male gender, and liver stiffness values were independent predictors of HCC (all P<0.05), whereas hepatitis B virus DNA ≥20,000 IU/L showed borderline statistical significance (P=0.0659). We developed a predictive model for HCC using these four variables, which showed good discrimination capability, with an area under the receiver operating characteristic curve (AUROC) of 0.806 (95% confidence interval 0.738–0.874). We used the bootstrap method to assess discrimination. The AUROC remained largely unchanged between iterations, with an average value of 0.802 (95% confidence interval 0.791–0.812). The predicted risk of occurrence of HCC calibrated well with the observed risk, with a correlation coefficient of 0.905 (P<0.001). CONCLUSION: This novel model accurately estimated the risk of HCC occurrence in patients with chronic hepatitis B.