Risk Prediction of Hepatocellular Carcinoma in Patients With Cirrhosis: The ADRESS-HCC Risk Model CANCER Flemming, J. A., Yang, J. D., Vittinghoff, E., Kim, W. R., Terrault, N. A. 2014; 120 (22): 3485-3493


All patients with cirrhosis are at risk of developing hepatocellular carcinoma (HCC). This risk is not uniform because other patient-related factors influence the risk of HCC. The objective of the current study was to develop an HCC risk prediction model to estimate the 1-year probability of HCC to assist with patient counseling.Between 2002 and 2011, a cohort of 34,932 patients with cirrhosis was identified from a national liver transplantation waitlist database from the United States. Cox proportional hazards regression methods were used to develop and validate a risk prediction model for incident HCC. In the validation cohort, discrimination and calibration of the model was examined. External validation was conducted using patients with cirrhosis who were enrolled in the Hepatitis C Antiviral Long-term Treatment against Cirrhosis (HALT-C) study.HCC developed in 1960 patients (5.6%) during a median follow-up of 1.3 years (interquartile range, 0.47 years-2.83 years). Six baseline clinical variables, including age, diabetes, race, etiology of cirrhosis, sex, and severity (ADRESS) of liver dysfunction were independently associated with HCC and were used to develop the ADRESS-HCC risk model. C-indices in the derivation and internal validation cohorts were 0.704 and 0.691, respectively. In the validation cohort, the predicted cumulative incidence of HCC by the ADRESS-HCC model closely matched the observed data. In patients with cirrhosis in the HALT-C cohort, the model stratified patients correctly according to the risk of developing HCC within 5 years.The ADRESS-HCC risk model is a useful tool for predicting the 1-year risk of HCC among patients with cirrhosis.

View details for DOI 10.1002/cncr.28832

View details for Web of Science ID 000344650900010

View details for PubMedID 25042049

View details for PubMedCentralID PMC4553222