Format

Send to

Choose Destination
J Hepatol. 2012 Mar;56(3):602-8. doi: 10.1016/j.jhep.2011.09.011. Epub 2011 Oct 23.

Data mining model using simple and readily available factors could identify patients at high risk for hepatocellular carcinoma in chronic hepatitis C.

Author information

1
Division of Gastroenterology and Hepatology, Musashino Red Cross Hospital, Tokyo, Japan.

Abstract

BACKGROUND & AIMS:

Assessment of the risk of hepatocellular carcinoma (HCC) development is essential for formulating personalized surveillance or antiviral treatment plan for chronic hepatitis C. We aimed to build a simple model for the identification of patients at high risk of developing HCC.

METHODS:

Chronic hepatitis C patients followed for at least 5 years (n=1003) were analyzed by data mining to build a predictive model for HCC development. The model was externally validated using a cohort of 1072 patients (472 with sustained virological response (SVR) and 600 with nonSVR to PEG-interferon plus ribavirin therapy).

RESULTS:

On the basis of factors such as age, platelet, albumin, and aspartate aminotransferase, the HCC risk prediction model identified subgroups with high-, intermediate-, and low-risk of HCC with a 5-year HCC development rate of 20.9%, 6.3-7.3%, and 0-1.5%, respectively. The reproducibility of the model was confirmed through external validation (r(2)=0.981). The 10-year HCC development rate was also significantly higher in the high-and intermediate-risk group than in the low-risk group (24.5% vs. 4.8%; p<0.0001). In the high-and intermediate-risk group, the incidence of HCC development was significantly reduced in patients with SVR compared to those with nonSVR (5-year rate, 9.5% vs. 4.5%; p=0.040).

CONCLUSIONS:

The HCC risk prediction model uses simple and readily available factors and identifies patients at a high risk of HCC development. The model allows physicians to identify patients requiring HCC surveillance and those who benefit from IFN therapy to prevent HCC.

PMID:
22027574
DOI:
10.1016/j.jhep.2011.09.011
[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center