Novel Multivariable Risk Score Outperforms FIB-4 for Identifying Patients At Risk of HCC

Ysabel Ilagan-Ying, MD

Credit: Yale School of Medicine

A new multivariable risk score may allow for improved hepatocellular carcinoma (HCC) risk assessment and mitigation in patients who are likely to be overlooked by current HCC screening practices, including those without viral hepatitis and advanced fibrosis or cirrhosis.¹

Leveraging data for > 6 million adults from US Department of Veterans Affairs (VA) electronic health records, the study identified several modifiable but frequently underrecognized risk factors associated with incident HCC and incorporated them into a risk score that outperformed Fibrosis-4 Index (FIB-4) alone for identifying patients at risk of HCC who do not have viral hepatitis or hepatic decompensation at baseline.¹

“To our knowledge, this study was the first modern large-scale research on the risk factors of HCC in adults without chronic viral hepatitis or hepatic decompensation that accounts for the level of liver fibrosis,” Ysabel Ilagan-Ying, MD, a clinical gastroenterology and hepatology fellow in the section of digestive diseases at Yale School of Medicine, and colleagues wrote.¹

Current HCC screening guidelines, specifically those from the American Gastroenterological Association and the American Association for the Study of Liver Diseases, may be missing opportunities for screening, early detection, and primary prevention targeting modifiable risk factors. With these recommendations, HCC is typically detected at advanced stages when treatment options are limited. Additionally, they focus largely on patients with viral hepatitis or diagnosed cirrhosis or require variables not routinely available in clinical care.^1,2,3

To identify modifiable HCC risk factors in the general population and develop a risk score to inform HCC screening and risk-factor modification interventions, investigators conducted a cohort study assessing demographic, clinical, laboratory, and diagnostic data from VA electronic health records for veterans 30-95 years of age without hepatitis B or C virus infection, hepatic decompensation, or prevalent HCC.¹

In total, the study included 6,509,288 veterans with a median age of 65 (Interquartile range, [IQR], 54-74) years who were predominantly male (92.9%) and non-Hispanic White (68.9%).¹

Participants were followed up until the occurrence of HCC diagnosis, death, or December 31, 2021. The primary outcome was the first HCC diagnosis during follow-up, defined using ICD-O-3 topography and histology codes from the VA national cancer registry as well as ICD-9 or ICD-10-CM codes requiring 1 inpatient diagnosis or > 1 outpatient diagnosis.¹

Data for the study cohort were divided into development and validation samples, with the development sample comprising 5,119,775 patients (2008-2011 and 2015-2020) and the validation sample comprising 1,389,513 patients (2012-2014). Investigators noted patient characteristics were similar between the 2 datasets.¹

The median follow-up time was 3.9 (IQR, 2.4-6.3) years in the development dataset and 7.8 (IQR, 4.5-8.8) years in the validation dataset. In the development sample, 14.2% of individuals had ≥ 10 years of follow-up. In total, 15,142 (0.2%) patients developed HCC, of whom 10,519 (69.5%) had FIB-4 of ≤ 3.25 at baseline.¹

While the incidence of HCC varied by FIB-4 level, variation of risk within FIB-4 strata was observed by levels of other risk factors. In multivariable analysis of the development sample, investigators noted FIB-4 was by far the most important factor, with an overall χ2 of 14 967, followed by diabetes status (χ2 = 2010) and age (χ2 = 899).¹

Investigators selected variables associated with HCC based on those identified in the literature but routinely available and directly analyzable within electronic health records, including age; sex; race and ethnicity; FIB-4; diabetes status; smoking status; alcohol use; and BMI.¹

When investigators compared the multivariable model using ordinal values to a model using continuous values and a decomposition of FIB-4 into its component variables, the model’s discrimination was 0.83 (95% CI, 0.82-0.85), while the best model using FIB-4 alone had a lower discrimination of 0.79 (95% CI, 0.77-0.80). Using the derived risk score as the only factor in Cox proportional hazards regression models, discrimination was 0.84 (95% CI, 0.83-0.84) in the development sample and 0.82 (95% CI, 0.82-0.82) in the validation sample.¹

Investigators noted the HCC risk score performed consistently well in the validation sample and across all subgroups, additionally pointing out the performance characteristics of this risk score were better than those of FIB-4 alone. Using FIB-4 > 3.25 as a threshold (5.0% of the sample), the sensitivity was 38.9% and PPV was 3.5%. For every 29 people screened, 1 true positive would be detected.¹

Using the model and a score > 58 (4.7% of the sample), the sensitivity was 45.1% and PPV was 4.3%. For every 23 people screened, 1 true positive would be detected, representing a 22.9% increase in cancers detected among those who were screened. Of those with risk scores ≥ 58, 48.6% had FIB-4 under 3.25; 19.5% were younger than 65 years of age; 44.0% did not have diabetes; and 56.7% reported abstinence from alcohol.¹

Investigators acknowledged multiple limitations to these findings, including the need for external validation of the model in other settings; the inability to identify confounding variables not routinely collected in electronic health records; intrinsic limitations to noninvasive scoring systems like FIB-4; and the potential overestimation of the degree of fibrosis in patients with ALD due to elevated liver transaminases.¹

“As the incidence of HCC continues to increase in tandem with the burden of obesity, the use of algorithms to stratify patients by HCC risk can guide patient-centered care,” investigators concluded.¹

References

1. ^{Ilagan-Ying YC, Gordon KS, Tate JP, et al. Risk Score for Hepatocellular Cancer in Adults Without Viral Hepatitis or Cirrhosis. JAMA Netw Open. 2024;7(11):e2443608. doi:10.1001/jamanetworkopen.2024.43608}
2. ^{Loomba R, Lim JK, Patton H, et al. AGA clinical practice update on screening and surveillance for hepatocellular carcinoma in patients with nonalcoholic fatty liver disease: expert review. Gastroenterology. 2020;158(6):1822-1830. doi:10.1053/j.gastro.2019.12.053}
3. ^{Singal AG, Llovet JM, Yarchoan M, et al. AASLD practice guidance on prevention, diagnosis, and treatment of hepatocellular carcinoma. Hepatology. 2023;78(6):1922-1965. doi:10.1097/HEP.0000000000000466}