Author information
1 Division of Gastroenterology, Seattle WA, United States; Department of Medicine Veterans Affairs Puget Sound Healthcare System and University of Washington, Seattle WA, United States; Research and Development, Veterans Affairs Puget Sound Healthcare System, Seattle WA, United States. Electronic address: georgei@medicine.washington.edu.
2 Research and Development, Veterans Affairs Puget Sound Healthcare System, Seattle WA, United States.
3 Department of Biostatistics, University of Washington, Seattle, WA, United States.
Abstract
BACKGROUND AND AIMS:
HCC risk varies dramatically in patients with cirrhosis according to well-described, readily-available predictors. We aimed to develop simple models estimating HCC risk in patients with ALD-cirrhosis or NAFLD-cirrhosis and calculate the net benefit that would be derived by implementing HCC surveillance strategies based on HCC risk as predicted by our models.
METHODS:
We identified 7068 patients with NAFLD-cirrhosis and 16,175 with ALD-cirrhosis who received care in the Veterans Affairs (VA) healthcare system in 2012 and retrospectively followed them until January 2018 for the development of incident HCC. We used Cox proportional hazards regression to develop and internally validate models predicting HCC risk using baseline characteristics at entry into the cohort in 2012. We plotted decision curves of net benefit against HCC screening threshold.
RESULTS:
We identified 1278 incident cases of HCC during a mean follow-up period of 3.7 years. Mean annualized HCC incidence was 1.56% in NAFLD-cirrhosis and 1.44% in ALD-cirrhosis. The final models estimating HCC were developed separately for NAFLD-cirrhosis and ALD-cirrhosis and included 7 predictors: age, gender, diabetes, BMI, platelet count, serum albumin and AST/√ALT ratio. The models exhibited very good measures of discrimination and calibration and area under the ROC curve of 0.75 for NAFLD-cirrhosis and 0.76 for ALD-cirrhosis. Decision curves showed higher standardized net benefit of risk-based screening using our prediction models compared to the screen-all approach.
CONCLUSIONS:
We developed simple models estimating HCC risk in patients with NAFLD-cirrhosis or ALD-cirrhosis, which are available as web-based tools (www.hccrisk.com). Risk stratification can be used to inform risk-based HCC surveillance strategies in individual patients or healthcare systems or to identify high-risk patients for clinical trials.
LAY SUMMARY:
Patients with cirrhosis of the liver are at risk of getting hepatocellular carcinoma (HCC or liver cancer) and therefore it is recommended that they undergo surveillance for HCC. However, the risk of HCC varies dramatically in patients with cirrhosis, which has implications on if and how patients get surveillance, how providers counsel patients about the need for surveillance, and how healthcare systems approach and prioritize surveillance. We used readily available predictors to develop models estimating HCC risk in patients with cirrhosis, which are available as web-based tools at www.hccrisk.com.